Method for defending inaudible instruction to control voice assistant based on machine learning
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A technology of machine learning and command control, which is applied in the security field of artificial intelligence voice assistants, and can solve problems such as long cycle, high difficulty, reducing screen brightness or volume, etc.
Active Publication Date: 2018-06-15
ZHEJIANG UNIV
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[0009] 5. Hidden attack: The screen display and voice feedback may expose the attack, but the hacker can reduce the screen brightness or volume to achieve the purpose of hiding
[0018] 1. Manufacturers are unable to improve the hardware of smart devices that have already been sold, or the cost of recall is unaffordable for manufacturers
[0019] 2. The ability of the microphone circuit to receive ultrasonic waves has always been a problem. Although with the progress a...
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[0059] Preferred embodiments of the present invention will be described in detail below.
[0060] figure 2 It is a comparison diagram of normal voice signal and malicious voice signal in the time-frequency domain; through figure 2 , it can be found that there is a significant difference in the high frequency band between the ordinary voice signal recorded by the mobile phone and the malicious signal (the demodulated signal). Therefore, malicious voice commands can be identified by machine learning algorithms. Such as image 3 with 4 As shown, the present invention provides targeted defense against "dolphin sound attack", and can detect the silent voice command of "dolphin sound attack", thereby fundamentally solving the possibility of voice assistants and smart devices being controlled by silent voice commands.
[0061] The following embodiment is a method for controlling a voice assistant based on machine learning defense against silent commands, and the defense device ...
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Abstract
The invention discloses a method for defending an inaudible instruction to control a voice assistant based on machine learning. The method comprises the following steps: 1) acquiring the data of positive and negative samples, 2) performing data segmentation on the positive and negative sample data; 3) performing sample feature selection and normalization; 4) selecting a classifier for training andgenerating a detection model used for detecting a malicious voice command; and 5) detecting the voice command to be detected through the obtained detection model. According to the invention, a creative feature selection method is selected, and the method is directed to intelligent devices with different models, and a normal voice command and the malicious voice command need to be acquired throughthe intelligent devices of the type, and a specific classifier for the type of device is trained as positive and negative samples. Through the customized mode, the problem that the detection and defense cannot be carried out by cross devices can be well solved.
Description
technical field [0001] The invention belongs to the security field of artificial intelligence voice assistants, and in particular relates to a method for controlling voice assistants based on machine learning defense silent commands. Background technique [0002] As a convenient and effective method of human-computer interaction, voice assistants such as Siri, Google Now, Alexa, Cortana, S Voice, and Hi Voice are becoming more and more popular in people's lives. Almost all smart devices (smart phones, tablets Computers, wearable devices and smart speakers) are equipped with corresponding voice assistants. [0003] However, voice assistants also face various security risks, such as a method called "dolphin sound attack" [Zhang, G., Yan, C., Ji, X., Zhang, T., Zhang, T.,&Xu,W.(2017).DolphinAtack:Inaudible Voice Commands.arXiv preprint arXiv:1708.09537.], using the vulnerability of the microphone circuit of the smart device, the voice assistant can be silently controlled to or...
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