Multi-dimensional anti-candid camera detector and detection method thereof

Abstract

The invention relates to the technical field of electronic equipment detection, in particular to a multi-dimensional anti-candid photographing detector and a detection method thereof. The system comprises a processing unit, a thermal imaging module, an electromagnetic radiation detection module and a wireless security module, wherein the wireless security module comprises at least two wireless network interface cards, and a first wireless network card is used for connecting a specified internal network and scanning and analyzing internal network equipment; the second wireless network card works in a monitoring mode and is used for sniffing all surrounding Wi-Fi signals, Bluetooth signals and associated equipment thereof and performing data packet analysis; the user interaction module, the positioning auxiliary module, the storage unit and the power supply module are used for providing electric energy for the detector. According to the invention, four technical dimensions of thermal imaging, wireless safety detection, infrared light detection and electromagnetic radiation detection are fused, and an intelligent analysis and positioning algorithm is combined, so that the comprehensiveness, the accuracy and the efficiency of detecting candid equipment are improved, and clear positioning guidance and comprehensive safety evaluation are provided for a user.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic equipment detection, and in particular to a multi-dimensional anti-sneak-photography detector and a detection method thereof. Background Art

[0002] With the rapid development of information technology and the widespread adoption of miniaturized and networked camera technology, the illegal installation of hidden cameras (commonly known as "pinhole cameras" or "sneak cams") for surreptitious filming has become increasingly common, seriously infringing on citizens' privacy and posing a threat to social security. These hidden cameras can be installed in private places such as hotel rooms, rental properties, fitting rooms, and public restrooms. Their high concealment and variety make their detection and prevention extremely challenging.

[0003] At present, the anti-secret camera detection methods and equipment available on the market mainly include the following:

[0004] Radio frequency (RF) signal detectors: These detectors locate cameras by detecting electromagnetic waves (such as Wi-Fi, Bluetooth, GSM, and other signals) emitted when the camera transmits video or audio signals wirelessly. The principle is to scan the strength of wireless signals in a specific frequency band. However, this method has obvious flaws: it cannot detect cameras that do not emit wireless signals, such as cameras that only store data locally (for example, on an SD card) or cameras with a wired connection; in environments with complex wireless signals (such as places filled with signals from routers, mobile phones, Bluetooth devices, etc.), false alarms are very likely to occur, making it difficult to accurately identify hidden cameras; some new hidden cameras may use frequency hopping technology, burst communications, or unconventional frequency bands, making them difficult for traditional RF detectors to capture.

[0005] Optical lens detectors (infrared light reflection detectors): These detectors use the optical properties of camera lenses to detect hidden cameras by emitting infrared or visible light of a specific wavelength and observing for specific reflection points (usually bright red or green spots) from the camera lens (the lens in front of the CCD / CMOS sensor). The principle is that light is reflected and refracted at the interface between different media. Camera lenses are usually made of multiple layers of optical glass or resin, which will produce identifiable reflections of specific light. However, this method also has its shortcomings:

[0006] The detection effect is greatly affected by the operator's experience and ambient light, and suspicious areas need to be scanned carefully and slowly. For cameras with very small lenses, cameras that are hidden too deeply, or cameras with filters or special coatings in front of the lenses, the detection effect will be significantly reduced. Other reflective objects in the environment (such as glass and metal surfaces) may also produce similar reflection points, causing misjudgment.

[0007] Network scanning-based detection methods: Some mobile apps or software tools attempt to identify suspicious networked cameras by scanning devices within the local area network (intranet) and analyzing information such as the device's MAC address, open ports, and device fingerprints. However, this method cannot detect cameras that are not connected to the user's current network, such as cameras that use independent SIM cards for 4G / 5G networking or cameras connected to other hidden Wi-Fi networks. The recognition accuracy is low for cameras that use non-standard protocols or deliberately hide their device characteristics. It also requires users to have certain network knowledge to make judgments, and the operation is relatively complex.

[0008] Thermal imaging detection (usually available as a standalone high-end device): When a camera is operating, its internal electronic components, such as the image sensor and processor, generate heat. Highly sensitive thermal imagers can detect these minute temperature differences, thereby detecting hidden cameras. However, current challenges include the high cost of professional, highly sensitive thermal imagers, making them unsuitable for widespread consumer use. Furthermore, the heat generated by miniature, low-power cameras is very weak and can be difficult to distinguish from other small heat sources in the environment (such as appliances in standby mode or wiring connections in walls). This is especially challenging when the camera is not operating continuously or is not dissipating heat properly.

[0009] In summary, existing anti-sneak camera detection technologies and equipment mostly rely on a single detection principle or a simple combination of functions. When faced with a wide variety of hidden cameras with ever-increasing concealment methods, they suffer from one or more of the following common problems:

[0010] Single detection dimension and incomplete coverage: It is difficult to effectively deal with all types of hidden cameras. For example, the detection capability is insufficient for non-wireless, low-power, deeply disguised cameras, or cameras connected to independent networks.

[0011] High false alarm and missed alarm rates: Single-feature judgment is easily affected by environmental interference or camera camouflage technology, resulting in the failure to detect real hidden cameras or misidentifying normal devices as hidden cameras.

[0012] Complex operations or reliance on user experience: Some detection methods require users to have professional knowledge or extensive troubleshooting experience, and have a high threshold for use.

[0013] Unable to effectively distinguish interference sources from real threats: For example, a simple RF detector cannot distinguish between a normal Wi-Fi router signal and the Wi-Fi signal of a wireless pinhole camera; a simple optical detector has difficulty distinguishing between reflections from a camera lens and reflections from other objects.

[0014] Difficulty in Positioning: Even if suspicious signals or features are detected, existing equipment often struggles to quickly and accurately locate the hidden camera. This can require significant time spent searching suspicious areas.

[0015] Therefore, there is an urgent need to develop a multi-dimensional anti-theft detection technology and device that can integrate multiple complementary detection principles, conduct comprehensive analysis and judgment of suspicious targets from multiple dimensions, so as to improve detection accuracy, expand detection range, lower the usage threshold, and effectively distinguish interference. Summary of the Invention

[0016] The purpose of the present invention is to address the problems existing in the background technology and propose a multi-dimensional anti-sneak peek detector and its detection method to solve the problems of the existing anti-sneak peek equipment in the technology, such as single detection means, low accuracy, easy omission, difficulty in accurate positioning and lack of intelligent guidance.

[0017] The present invention aims to provide a multi-dimensional anti-sneak peek detector and its detection method. By integrating four technical dimensions: thermal imaging, wireless security detection, infrared light detection, and electromagnetic radiation detection, and combining intelligent analysis and positioning algorithms, the comprehensiveness, accuracy, and efficiency of detecting sneak peek devices are improved, and clear positioning guidance and comprehensive security assessments are provided to users.

[0018] The technical solution of the present invention, in a first aspect, provides a multi-dimensional anti-sneak camera detector, comprising:

[0019] Processing unit: used to control the operation of the detector, process data from each module, execute analysis algorithms, and output results on the user interaction module;

[0020] Thermal imaging module: used to capture heat distribution in the environment, identify electronic devices that generate heat due to operation, and display thermal imaging images on the user interaction module;

[0021] Infrared light detection module: used to emit infrared light and receive the "cat's eye effect" light spots formed by the reflection of the camera lens;

[0022] Electromagnetic radiation detection module: used to detect the intensity of electromagnetic radiation generated by electronic equipment when it is working;

[0023] Wireless security module: includes at least two wireless network interface cards, the first of which is used to connect to a designated internal network and perform intranet device scanning and analysis; the second wireless network card works in monitoring mode to sniff all surrounding Wi-Fi signals, Bluetooth signals and their associated devices, and perform data packet analysis;

[0024] User interaction module: including a touch screen display for displaying the operation interface, detection information, thermal imaging screen, positioning guidance and detection reports;

[0025] Positioning assistance module: includes several inertial measurement units, which are used to sense the device's own posture and movement and perform auxiliary positioning;

[0026] Storage unit: used to store firmware, hidden camera device feature database, and detection logs;

[0027] Power module: used to provide power to the detector.

[0028] Preferably, the infrared detection module further includes an infrared light source, a narrowband filter and an image sensor, and the narrowband filter is used to reduce ambient light interference.

[0029] Preferably, after the user starts the detector, the user needs to set the detection parameters through the user interaction module.

[0030] Preferably, the first wireless network card is connected to the internal Wi-Fi network specified by the user; the network scanning tool is used to scan the internal network, collect the IP addresses, MAC addresses, open ports, and operating system information of all active devices in the internal network, and compare them with the built-in database of known hidden camera device features.

[0031] Preferably, the second wireless network card enters monitoring mode, continuously captures Wi-Fi signals in the surrounding 2.4GHz and 5GHz frequency bands and MAC addresses of client devices connected to these Wi-Fi networks; and analyzes the captured external Wi-Fi traffic.

[0032] Preferably, after entering the monitoring mode, the second wireless network card can be extended to monitor broadcast information of wireless protocols including Bluetooth.

[0033] A second aspect of the present invention provides a detection method for a multi-dimensional anti-sneak camera detector, which is applied to the above-mentioned detector and includes the following specific steps:

[0034] S1. The user starts the detector and sets the detection parameters for wide-area wireless detection;

[0035] S2. Analyze the detection results to determine the probability of each detected device being a hidden camera device; for highly suspected wireless devices, measure the received signal strength indicator of the suspicious device's signal reaching the network card to perform preliminary positioning and determine the search direction;

[0036] S3, used to guide the handheld detector to move towards the suspicious area and prompt the user to perform a detailed scan when approaching suspicious equipment;

[0037] S4. Activate the thermal imaging module to scan and determine if the electronic equipment is in working condition.

[0038] S5. Enable the infrared light detection module to scan and determine the camera lens within the detection range;

[0039] S6. Activate the electromagnetic radiation detection module to scan and determine the electromagnetic radiation of electronic devices within the detection range;

[0040] S7: The processing unit fuses the detection results, outputs the probability that a hidden camera device exists in the area, and displays it through the user interaction module;

[0041] S8. Generate an analysis report for the detection results for user analysis.

[0042] Preferably, in step S2, samples are taken based on common hidden camera devices on the market and a hidden camera device feature database is established; by comparing with the data in the database, the probability that each detected device is a hidden camera device is calculated; the database contains known hidden camera device MAC address ranges, device fingerprints, communication protocol features, and default port data.

[0043] Preferably, in step S2, the dual network cards of the wireless security module respectively measure the received signal strength indication of the suspicious device signal reaching their respective network cards; the processing unit uses the RSSI values of the dual network cards, combined with the principle of the dual base station RSSI positioning algorithm or the triangulation positioning algorithm, to preliminarily estimate the approximate direction and distance of the suspicious device.

[0044] Preferably, the user interaction module displays a list of suspicious devices and their risk levels, and prompts the user to pick up the device and move towards the estimated direction of the suspicious device.

[0045] Compared with the prior art, the present invention has the following beneficial technical effects:

[0046] 1. The present invention integrates four detection methods: thermal imaging, wireless security (intranet + extranet), infrared light and electromagnetic radiation, and constructs a multi-dimensional detection capability. The thermal imaging detection module can effectively identify cameras that generate heat due to work, including local storage cameras that do not emit wireless signals or cameras with low power consumption and weak heat characteristics, which makes up for the detection blind spots of traditional detectors for such targets. The wireless network and intranet detection module can not only actively scan and identify standard Wi-Fi, Bluetooth and other wireless cameras, but also discover more types of networked cameras by analyzing the radio frequency signal characteristics of specific frequency bands and the communication behavior, port characteristics and device fingerprints of devices in the local area network, including some devices that use non-standard protocols or deliberately hide their network identities. The infrared light auxiliary detection module uses the unique optical reflection principle of the camera lens to effectively detect cameras that are not working, have no network connection or no obvious heat, and can serve as an important physical verification method after other detection modes find suspicious points.

[0047] 2. Through the organic combination of the above-mentioned multi-dimensional detection capabilities, the present invention can cope with cameras including but not limited to local storage type, various wireless networking types (Wi-Fi, Bluetooth, etc.), wired networking type, low-power type, and even partially disguised or dormant state cameras, greatly improving the breadth and depth of detection and effectively reducing the missed reporting rate.

[0048] 3. Utilizing the aforementioned multi-dimensional information, including thermal imaging, wireless signals, infrared reflections, and electromagnetic radiation, cross-validation, combined with intelligent analysis algorithms and a feature database, can more accurately identify hidden camera devices, effectively eliminating interference from normal electronic devices and reducing false alarm rates. Initial positioning based on dual network card RSSI and user movement, combined with dynamic, optimized positioning using the gyroscope, provides users with clear direction and distance guidance, helping them quickly approach suspicious targets and improving search efficiency.

[0049] 4. The specific 750nm narrowband filter combined with the 750nm infrared fill light can effectively reduce the interference of ambient stray light and improve the detection sensitivity and accuracy of the tiny camera lens under complex lighting conditions.

[0050] 5. The integrated design, touch screen operation interface and clear test report make it easy for ordinary users to operate and understand the test results.

[0051] 6. Provide comprehensive test reports and safety recommendations to help users fully understand the safety status of their environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a structural block diagram of a multi-dimensional anti-sneak-camera detector according to an embodiment of the present invention;

[0053] Figure 2 Flowchart of a multi-dimensional anti-sneak photography detection method according to an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of preliminary positioning of a wireless security module according to an embodiment of the present invention;

[0055] Figure 4 This is one of the schematic diagrams of the user interaction interface in an embodiment of the present invention;

[0056] Figure 5 This is the second schematic diagram of the user interaction interface in the embodiment of the present invention. DETAILED DESCRIPTION

[0057] Example 1

[0058] like Figure 1 As shown, the first aspect of the present invention provides a multi-dimensional anti-sneak camera detector, comprising:

[0059] Processing unit: used to control the operation of the detector, process data from each module, execute analysis algorithms, and output results on the user interaction module;

[0060] Thermal imaging module: used to capture heat distribution in the environment, identify electronic devices that generate heat due to operation, and display thermal imaging images on the user interaction module;

[0061] Infrared light detection module: used to emit infrared light and receive the "cat's eye effect" light spots formed by reflection from the camera lens; the infrared detection module also includes an infrared light source (such as a 750nm infrared light fill light), a narrowband filter (such as a 750nm narrowband filter) and an image sensor. The narrowband filter reduces ambient light interference.

[0062] Electromagnetic radiation detection module: used to detect the intensity of electromagnetic radiation generated by electronic equipment when it is working;

[0063] Wireless security module: includes at least two wireless network interface cards (NICs), the first of which is used to connect to a designated internal network (such as hotel Wi-Fi) and perform intranet device scanning and analysis; the second wireless network card operates in monitor mode to sniff all surrounding Wi-Fi signals, Bluetooth signals and their associated devices, and perform data packet analysis;

[0064] User interaction module: including a touch screen display for displaying the operation interface, detection information, thermal imaging screen, positioning guidance and detection reports;

[0065] Positioning assistance module: includes several inertial measurement units (such as gyroscope sensors) to sense the device's own posture and movement and perform auxiliary positioning;

[0066] Storage unit: used to store firmware, hidden camera device feature database, and detection logs;

[0067] Power module: used to provide power to the detector.

[0068] In this embodiment, after the user starts the detector, he needs to set the detection parameters through the user interaction module, such as connecting to the target hotel Wi-Fi;

[0069] In this embodiment, the first wireless network card connects to a user-specified internal Wi-Fi network. A network scanning tool is used to scan this internal network, collecting the IP addresses, MAC addresses, open ports, and operating system information of all active devices on the network. This information is then compared against a built-in database of known hidden camera device signatures. The second wireless network card enters monitoring mode, continuously capturing Wi-Fi signals in the surrounding 2.4GHz and 5GHz bands, as well as the MAC addresses of client devices connected to these Wi-Fi networks. This captured external Wi-Fi traffic is analyzed. Once in monitoring mode, the second wireless network card can also monitor broadcasts of wireless protocols, including Bluetooth.

[0070] This embodiment integrates four detection methods: thermal imaging, wireless security (intranet + extranet), infrared light, and electromagnetic radiation, to build a multi-dimensional detection capability. Through the organic combination of multi-dimensional detection capabilities, it can handle cameras including but not limited to local storage, various wireless networking types (Wi-Fi, Bluetooth, etc.), wired networking types, low-power types, and even partially camouflaged or dormant cameras, greatly improving the breadth and depth of detection and effectively reducing the missed reporting rate.

[0071] Example 2

[0072] like Figure 2 As shown, this embodiment provides a detection method of a multi-dimensional anti-sneak camera detector, which is applied to the detector in Example 1 and includes the following specific steps:

[0073] S1. The user starts the detector and sets the detection parameters for wide-area wireless detection; for example, connecting to the target hotel Wi-Fi;

[0074] The first wireless network card connects to the user's designated internal Wi-Fi network. This network is scanned using a network scanning tool (e.g., based on Nmap principles). The IP addresses, MAC addresses, open ports, and operating system information of all active devices on the network are collected and initially compared with a built-in database of known hidden camera device characteristics (e.g., MAC address segments of specific manufacturers, specific open port combinations, and suspicious device names).

[0075] The second wireless network card enters monitoring mode, continuously capturing Wi-Fi signals in the surrounding 2.4GHz and 5GHz bands (including SSID, BSSID, channel, encryption method, etc.) and the MAC addresses of client devices connected to these Wi-Fi networks. It can also be expanded to monitor broadcast information from other wireless protocols such as Bluetooth.

[0076] Perform preliminary analysis of captured external Wi-Fi traffic (especially traffic from suspicious devices, such as known camera brands identified by MAC addresses), detecting video stream characteristics and unusual connection behavior. This step quickly provides an understanding of the overall network status of the current environment and identifies potentially risky wireless devices within and outside the network, providing targets for subsequent, accurate detection. Simultaneous operation of dual network cards improves detection efficiency.

[0077] S2. Analyze the detection results to determine the probability of each detected device being a hidden camera device; for highly suspected wireless devices, measure the received signal strength indicator of the suspicious device's signal reaching the network card to perform preliminary positioning and determine the search direction;

[0078] Specifically, the processing unit can use the information collected in S1, including both internal and external wireless device information, to perform in-depth comparison and intelligent analysis with the built-in "hidden camera device feature database" to calculate the probability that each detected device is a hidden camera device. This database contains information such as the MAC address range, device fingerprints, communication protocol features, and default ports of known hidden camera devices.

[0079] If a high-probability suspicious wireless device is detected, the wireless security module's dual network cards measure the received signal strength indicator (RSSI) of the suspicious device's signal reaching their respective network cards. The processing unit uses the RSSI values of the dual network cards, combined with a dual-base station RSSI positioning algorithm or a triangulation positioning algorithm, to preliminarily estimate the approximate direction and distance of the suspicious device. Due to the limited accuracy of initial positioning, it mainly provides the user with a general search direction. The user interaction module displays a list of suspicious devices and their risk levels on the screen, and prompts the user to pick up the device and move toward the estimated direction of the suspicious device.

[0080] The basic principle of RSSI positioning relies on the model of signal strength attenuation with distance, such as the free space path loss formula L fs =20log 10 (d)+20log 10 (f) + K, where d is the distance, f is the frequency, and K is a constant. Distance d is inferred from RSSI. Triangulation requires distance information from at least three known points (here, dual network cards can be considered two points, with user movement forming multiple measurement points) to determine the target location.

[0081] This step can screen out key suspects from a large number of wireless devices, and use dual network cards and positioning algorithms to provide users with preliminary direction guidance, narrow the search range, and improve positioning efficiency.

[0082] S3, used to guide the handheld detector to move towards the suspicious area and prompt the user to perform a detailed scan when approaching suspicious equipment;

[0083] The user moves the handheld detector toward the suspicious area according to the guidance of S2. The gyroscope of the positioning auxiliary module senses the movement direction and posture changes of the device in real time.

[0084] The processing unit combines the dynamic changes in the RSSI values of the dual network cards and gyroscope data during user movement to continuously optimize the prediction of the location of suspicious wireless devices, and displays the estimated distance and direction arrow in real time on the user interaction module to guide the user to gradually approach.

[0085] When the detector determines that the user is close to a suspicious device (for example, the RSSI value reaches a certain threshold, or the estimated distance is less than the set value), the user is prompted to enable other detection dimensions for detailed scanning.

[0086] S4. Activate the thermal imaging module to scan and identify any electronic devices in operation. The user points the device at the suspicious area, and the screen displays the thermal image in real time. The processing unit analyzes the thermal imaging data and highlights abnormally hot spots, which are typically active electronic devices, including hidden camera devices.

[0087] S5. Enable the infrared light detection module to scan and identify camera lenses within detection range. To perform an infrared light detection module scan, the user points the device at the suspicious area. The detector activates a 750nm infrared fill light and captures the reflected light through the image sensor behind a 750nm narrowband filter. If a camera lens is in the area, it will appear as a bright spot on the screen (a cat's eye effect). The narrowband filter effectively filters out other wavelengths of light in the environment, improving the signal-to-noise ratio and enhancing the detection capability of even tiny lenses.

[0088] S6. Activate the electromagnetic radiation detection module to scan and determine the electromagnetic radiation of electronic devices within the detection range. Electromagnetic radiation detection module scanning: The user points the handheld device at the suspected area, and the electromagnetic radiation detection module detects the electromagnetic radiation intensity in the area. If there are any operating electronic devices (especially wireless transmitting devices), strong electromagnetic radiation will be detected.

[0089] S7. The processing unit integrates the detection results and outputs the probability of a hidden camera being present in the area, which is displayed via the user interaction module. The processing unit integrates data from the thermal imaging, infrared light, and electromagnetic radiation detection modules to make a comprehensive judgment. For example, if an area simultaneously exhibits abnormal heating, infrared cat's eye effect, and strong electromagnetic radiation, the probability of a hidden camera being present is extremely high.

[0090] This step transitions from general directional guidance to close-range precision scanning by integrating user movement and multi-sensor data. The combination of multiple detection methods significantly increases the probability of detecting hidden cameras (including wired, wireless, and storage-based devices), effectively distinguishes interference sources, and reduces false alarms. The use of narrowband filters and specific wavelength fill lights in infrared light detection significantly improves the ability to identify cameras in complex lighting environments.

[0091] S8. Generate an analysis report for the detection results for the user to analyze. After completing all the above detection steps, regardless of whether a clear hidden camera device is found, the processing unit will conduct a comprehensive analysis of all collected data (wireless environment, suspicious devices, hot spots, infrared reflection points, electromagnetic radiation intensity, etc.).

[0092] Generate a detailed test report and display it to the user through the user interaction module. The report includes:

[0093] An overall security level assessment of the current environment (e.g., safe, low risk, medium risk, high risk).

[0094] List of discovered intranet devices and their basic information.

[0095] Detected surrounding Wi-Fi and Bluetooth device information.

[0096] Marked high-risk suspicious points (if any).

[0097] Summary of the detection results of each dimension.

[0098] Security considerations and recommendations for the current environment.

[0099] This step provides a clear and comprehensive detection report to help users understand the security status of the current environment and highlight potential risk points. Even if users do not directly find the device, they can make more informed judgments and take precautions based on the report.

[0100] In addition, in wireless security modules, besides using dual Wi-Fi network cards, consideration can be given to integrating Bluetooth and Zigbee sniffing modules to cover a wider range of wireless hidden camera devices. Regarding positioning algorithms, further research can be conducted on more refined positioning methods based on channel state information (CSI), or by combining them with other sensors (such as ultrasonic ranging) for auxiliary positioning. Infrared light detection modules can also consider using infrared light of different wavelengths and corresponding filter combinations to accommodate lenses with different coatings.

[0101] To make the objectives, technical solutions and advantages of the present invention more clear, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0102] Reference Figure 1, an embodiment of the present invention provides a multi-dimensional anti-sneak peek detector, including: a central processing unit (CPU / SoC), which serves as the core of the detector and connects and controls all other modules. A thermal imaging module, such as a far-infrared thermal imaging sensor of the Hikvision TB4117 series or similar specifications, can sense infrared radiation with a wavelength of 8-14μm and convert temperature differences into visible thermal images. A wireless security module with two built-in Wi-Fi chips. The first Wi-Fi chip (such as the Wi-Fi function of ESP32) is used to connect to the hotel Wi-Fi network specified by the user, execute a network scanning program based on nmap, and obtain the IP, MAC, open ports (such as common camera ports such as 80, 8080, 554RTSP, etc.) and service fingerprints of intranet devices. The second Wi-Fi chip (such as the RTL8812AU chip that supports monitor mode) is set to monitor mode. Using a similar principle to Airodump-ng, it captures beacon frames, probe request / response frames, and data frames from all nearby Wi-Fi networks, extracts the MAC addresses of APs and stations, and performs shallow or deep packet inspection (DPI) on traffic from specific suspicious MAC addresses to identify video stream characteristics. The infrared light detection module consists of one or more 750nm infrared LED arrays, a 750±10nm narrowband pass filter, and a visible light CMOS / CCD image sensor (such as a mobile phone camera module). During operation, the infrared LEDs emit 750nm infrared light. After reflection from an object, only light near 750nm passes through the filter and reaches the image sensor. Because the camera lens (especially the filter in front of the CCD / CMOS sensor or the lens itself) has a high reflectivity in specific near-infrared bands, bright spots (the cat's eye effect) are formed. The electromagnetic radiation detection module uses a broadband antenna with a detection circuit and amplifier to detect the intensity of electromagnetic waves leaked by nearby electronic devices during operation and quantify it into a specific value or level. The user interaction module uses a 3.5-inch or 5-inch LCD touchscreen to display a graphical user interface (GUI). Users can operate the module through touch to view real-time thermal imaging, wireless scanning results, infrared detection images, electromagnetic radiation intensity indicators, and the final detection report. The positioning assistance module integrates a six-axis or nine-axis inertial measurement unit (IMU), such as the MPU6050, providing gyroscope and accelerometer data to assist in calculating the device's direction and distance as the user moves the device, thereby optimizing the location of wireless sources. The storage unit, such as an eMMC or SD card, is used to store the operating system, applications, a database of hidden device characteristics (including known device MAC address prefixes, default SSID modes, specific port combinations, known firmware fingerprints, etc.), user settings, and detection logs. The power module is powered by a rechargeable lithium battery and equipped with appropriate charge and discharge management circuitry.

[0103] Specific detection process (refer to Figure 2 ):

[0104] Startup and network detection (S1): The user turns on the detector in the hotel room. Select "Start Detection" on the touch screen and enter the SSID and password of the current hotel Wi-Fi as prompted. The detector's first wireless network card connects to the hotel Wi-Fi. Then, the processing unit calls the nmap tool to scan the connected Wi-Fi network segment (for example, 192.168.1.0 / 24), identify all online hosts, obtain their IP addresses, MAC addresses, open ports (such as TCP 80, 443, 554, 8080, UDP 5000, etc.), and attempt to obtain operating system or device type information. At the same time, the second wireless network card automatically enters monitoring mode, scanning all surrounding 2.4GHz and 5GHz band Wi-Fi signals, and recording the SSID, BSSID, channel, encryption method, and signal strength of each AP. In addition, it also monitors the MAC addresses of client devices connected to these APs, as well as the MAC addresses of devices that are not connected to the AP but have issued ProbeRequests. The processing unit compares the collected MAC addresses with the built-in OUI (Organizationally Unique Identifier) list of known hidden camera device manufacturers.

[0105] Suspicious wireless target analysis and preliminary positioning (S2): The processing unit integrates the intranet scanning results and external Wi-Fi monitoring results. For example, if a MAC address in the intranet belongs to a known network camera brand and the RTSP (554) port is open, it will be marked as highly suspicious. For another example, if a Wi-Fi signal with a hidden SSID is monitored externally, its MAC address also matches the characteristics of a known hidden camera device, and there is a client connected, it is also marked as highly suspicious. For wireless devices marked as highly suspicious, the detector measures the RSSI value of its signal through two network cards respectively. Assume that the first network card measures RSSI1 and the second network card measures RSSI2. The processing unit preliminarily estimates the direction of the suspicious device based on the difference and absolute value of RSSI1 and RSSI2, combined with a simplified path loss model. For example, if RSSI1>RSSI2, the target may be closer to the side of the detector where the first network card is located. An arrow pointing to the general direction will be displayed on the screen. At this time, the screen will prompt the user: "A suspicious wireless device has been found. Please move the detector in the direction of the arrow."

[0106] Guided Movement and Near-Field Multi-Dimensional Scanning (S3): The user holds the detector and slowly moves according to the arrows on the screen. The IMU module monitors the device's direction and speed in real time. The processing unit continuously adjusts the direction guidance based on the RSSI value trend (increases with approach and decreases with distance) and IMU data. When the RSSI value reaches a preset high-intensity threshold (e.g., -40dBm), or the estimated distance is less than 1-2 meters, the screen prompts: "Approaching a suspicious area, please enable fine scanning." The user clicks the "Fine Scan" button, or the detector automatically switches modes.

[0107] Thermal Imaging Scan: The screen switches to a live feed from the thermal imaging module. The user points the detector at a suspected object or area (such as a wall socket, decorative painting, or smoke detector). If the hidden camera is operating, it typically generates heat, which appears as a brighter area on the thermal image.

[0108] Infrared Scanning: The user switches to infrared scanning mode. The detector's 750nm infrared LED illuminates, and the image sensor forms an image through a 750nm narrowband filter. The user slowly scans the suspicious area. If a camera lens is facing the detector within the field of view, one or more bright spots (a cat's eye effect) will appear clearly on the screen.

[0109] Electromagnetic Radiation Scan: The user switches to electromagnetic radiation detection mode. The screen displays the electromagnetic radiation intensity in the area in front of the detector as a bar graph or numerical value. The reading will be significantly higher if the detector is near operating electronic devices (especially those with wireless transmission capabilities).

[0110] The processing unit combines the results of these three near-field scans. For example, if thermal imaging shows a slight heat buildup near a socket, and infrared scanning detects a cat's-eye effect near a small hole in the socket, along with high electromagnetic radiation intensity, it can be safely assumed that a hidden camera is present.

[0111] Results Summary and Report (S4): After completing the scan, the user can choose to end the test. The detector will generate a test report, which will be displayed on the screen and can be saved. The report includes:

[0112] Environmental safety assessment: For example, “High risk, suspected hidden camera features were found at location XX.”

[0113] Intranet device list: IP, MAC, open ports, manufacturer (if identifiable).

[0114] Surrounding Wi-Fi environment: SSID list, signal strength, and encryption status.

[0115] Suspicious target details: positioning information, detection evidence in various dimensions.

[0116] Safety advice: such as "Please carefully check the sockets and decorations in the XX area", "It is recommended to disconnect the power supply of suspicious equipment", etc.

[0117] Through the above-mentioned multi-dimensional and multi-stage detection method, the present invention can significantly improve the success rate and accuracy of anti-sneak photography, and provide users with convenient operation and clear guidance.

[0118] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A multi-dimensional anti-sneak camera detector, characterized in that: include: Processing unit: used to control the operation of the detector, process data from each module, execute analysis algorithms, and output results on the user interaction module; Thermal imaging module: used to capture heat distribution in the environment, identify electronic devices that generate heat due to operation, and display thermal imaging images on the user interaction module; Infrared light detection module: used to emit infrared light and receive the "cat's eye effect" light spots formed by the reflection of the camera lens; Electromagnetic radiation detection module: used to detect the intensity of electromagnetic radiation generated by electronic equipment when it is working; Wireless security module: includes at least two wireless network interface cards, the first of which is used to connect to a designated internal network and perform intranet device scanning and analysis; the second wireless network card works in monitoring mode to sniff all surrounding Wi-Fi signals, Bluetooth signals and their associated devices, and perform data packet analysis; User interaction module: including a touch screen display for displaying the operation interface, detection information, thermal imaging screen, positioning guidance and detection reports; Positioning assistance module: includes several inertial measurement units, which are used to sense the device's own posture and movement and perform auxiliary positioning; Storage unit: used to store firmware, hidden camera device feature database, and detection logs; Power module: used to provide power to the detector.

2. The multi-dimensional anti-sneak-photographing detector according to claim 1, characterized in that: The infrared detection module also includes an infrared light source, a narrow-band filter and an image sensor, and the narrow-band filter is used to reduce ambient light interference.

3. The multi-dimensional anti-sneak-photographing detector according to claim 1, characterized in that: After the user starts the detector, he needs to set the detection parameters through the user interaction module.

4. The multi-dimensional anti-sneak camera detector according to claim 1, characterized in that: The first wireless network card connects to the internal Wi-Fi network specified by the user; a network scanning tool is used to scan the internal network, collect the IP addresses, MAC addresses, open ports, and operating system information of all active devices in the internal network, and compare them with the built-in database of known hidden camera device characteristics.

5. A multi-dimensional anti-sneak-camera detector according to claim 1 or 4, characterized in that: The second wireless network card enters monitoring mode and continuously captures the surrounding 2.4GHz and 5GHz Wi-Fi signals and the MAC addresses of client devices connected to these Wi-Fi networks; Analyze captured external Wi-Fi traffic.

6. The multi-dimensional anti-sneak-photographing detector according to claim 1, characterized in that: After the second wireless network card enters the monitoring mode, it can be expanded to monitor broadcast information of wireless protocols including Bluetooth.

7. A detection method for a multi-dimensional anti-sneak-camera detector, applied to the detector according to any one of claims 1 to 6, characterized in that: The specific steps include: S1. The user starts the detector and sets the detection parameters for wide-area wireless detection; S2. Analyze the detection results to determine the probability that each detected device is a hidden camera device; For high-probability suspicious wireless devices, measure the received signal strength indicator of the suspicious device's signal reaching the network card to perform preliminary positioning and determine the search direction; S3, used to guide the handheld detector to move towards the suspicious area and prompt the user to perform a detailed scan when approaching suspicious equipment; S4. Activate the thermal imaging module to scan and determine if the electronic equipment is in working condition. S5. Enable the infrared light detection module to scan and determine the camera lens within the detection range; S6. Activate the electromagnetic radiation detection module to scan and determine the electromagnetic radiation of electronic devices within the detection range; S7: The processing unit fuses the detection results, outputs the probability that a hidden camera device exists in the area, and displays it through the user interaction module; S8. Generate an analysis report for the detection results for user analysis.

8. The detection method of a multi-dimensional anti-sneak-camera detector according to claim 7, characterized in that: In step S2, samples are taken from common hidden camera devices on the market and a hidden camera device feature database is established; by comparing with the data in the database, the probability of each detected device being a hidden camera device is calculated; the database contains known hidden camera device MAC address ranges, device fingerprints, communication protocol features, and default port data.

9. The detection method of a multi-dimensional anti-sneak-camera detector according to claim 7, characterized in that: In step S2, the dual network cards of the wireless security module respectively measure the received signal strength indication of the suspicious device signal reaching their respective network cards; the processing unit uses the RSSI values of the dual network cards, combined with the principles of the dual base station RSSI positioning algorithm or the triangulation positioning algorithm, to preliminarily estimate the approximate direction and distance of the suspicious device.

10. The detection method of a multi-dimensional anti-sneak-camera detector according to claim 7, characterized in that: The user interaction module displays a list of suspicious devices and their risk levels, and prompts the user to pick up the device and move towards the estimated suspicious device.

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