70results about How to "Increase imperceptibility" patented technology

The invention discloses an audio spread spectrum watermark processing method for protecting the audio data, comprising two processes: a watermark embedding process and a watermark extraction process; in the embedding process, a wavelet de-noising technique is used for separating the noise of the signal and noise intensity control factor is used for reducing the noise signal intensity, and the effect of the noise of the signal on the detector performance is reduced; in order to improve the performance of the detector, the wavelet de-noising technique is also used in the water extraction process; in order to overcome the problem of excessively large correlation sliding detection calculation quantity in the corresponding detection, a synchronous positioning mechanism is designed. The algorithm is applicable to music and speech, which can effectively resist the attack of normal signal processing, more particularly, the attack of DA/AD conversion, thus being applied to the fields such as audio copyright protection, temper-proofing, certification, etc.

The invention discloses an image watermarking embedding method based on singular value decomposition and principal component analysis. The method includes: S1a, conducting scrambling processing on the original watermarking image W and obtaining the scrambled watermarking image W' ; S2a, conducting blocking on an original carrier image I, dividing the original carrier image I into an image block C of 8X8 and further obtaining original carrier image blocks; S3a, conducting the singular value decomposition on the original carrier image blocks C and obtaining the singular value of each image block; S4a, taking the singular value of each image block as the eigenvalue of the principal component analysis, conducting the principal component analysis on the singular value, extracting the component Y of the principal component whose the contribution rate reaches above 99.99%; and S5a, embedding watermarks in the component Y of the principal component, obtaining the component Y' of the principal component embedded with the watermarking image information, conducting inverse transformations of the principal component analysis and the singular value decomposition on Y', and obtaining the carrier image I' embedded with the watermarking.

The invention discloses a machine learning-based color image watermark embedding and detecting method. The method comprises the following steps of firstly, using a matrix-based image normalizing technology to map a carrier image to a geometric invariant space, and combining a invariant centroid theory to determine an important area in a normalized color image; secondly, performing non-subsample Contourlet conversion on the important area of the G-component normalized color image; finally, according to a G-component visual mask model, adaptively determining embedding intensity, so that an image watermark is embedded in a low-frequency area, and the embedded watermark is guaranteed to have very good transparency and very high robustness; during watermark detection, by using the height relativity among color image components, selecting a stable feature vector training SVR (Support Vector Regression) model, and using the SVR training model to extract digital watermark information.

The invention provides a radar data protection method based on the combination of information hiding and encryption techniques. The radar data protection method comprises the steps of hiding the encrypted radar data information in a digital image, and modifying the filename extension to further hide the file with the hidden radar data. According to the method, three barriers for protecting the radar data are established, and therefore, the probability that the confidential radar information is cracked can be effectively reduced and the security of the radar data can be protected better.

The invention discloses a robust digital audio watermark embedding and detection method based on polar harmonic transform. Advantages of great frequency domain localization features, multi-scale, redundancy and translation invariance, the features of non-stationary of greatly describing signals and maintaining the shape of the signals and the same length of audio frequency of each sub-band after decomposition with that of the original audio frequency of nonsubsampled wavelet transform are utilized; then great numerical stability of polar harmonic transform is also utilized, and watermarks are embedded in stable torque values to resist the normal signal attack; and then the synchronization code technology is combined. The experimental results indicate that normal signal processing can be resisted and the desynchronization attack can also be effectively resisted so that the method has great imperceptibility and robustness. Besides, the method also has advantages of being simple in design and easy to realize.

The invention relates to image processing, the invention relates to the technical field of digital watermarking, and discloses a high-capacity reversible image watermarking algorithm with improved difference expansion. The algorithm comprises the steps of watermark embedding and watermark extraction, watermark embedding of an original image and watermark extraction of an image containing watermarks through cat face scrambling, a generalized difference expansion method, improved gray scale overflow processing and a difference quantification method. Compared with the prior art, the algorithm hasthe advantages that the pixel mean value before and after watermark embedding through a generalized difference expansion method is invariant, so that the smoothness value before and after watermark embedding is invariant, and watermark extraction is facilitated; an improved gray overflow processing method is used, on the premise that certain visual quality is guaranteed, watermark embedding can be conducted multiple times, and the watermark embedding capacity is increased; auxiliary information is embedded through a difference quantization algorithm, so that the method has relatively good imperceptibility and relatively high effective load capacity; and after the watermark is extracted, the original image can be completely recovered, the similarity between the extracted watermark and theoriginal watermark is extremely high, and the error is small.

The invention discloses a high-security steganography method for voice over IP, which includes the following steps: a sender reads original sampled voice, and compresses and encodes the original sampled voice to get a carrier voice sequence; the sender extracts an intensity sequence and an amplitude sequence of the carrier voice sequence, divides the carrier voice sequence into a silent interval and a non-salient interval, and defines the distortion costs for different carrier voice intervals; the sender constructs a ladder generation matrix according to the length and embedding rate of the carrier voice sequence; the sender embeds secret information according to the distortion costs and the generation matrix, calculates out a modification vector minimizing carrier distortion, and modifiesthe carrier voice sequence according to the modification vector obtained to get a secret information carrying voice sequence; and after receiving the secret information carrying voice sequence, a receiver recovers a secret information sequence according to the ladder generation matrix. Stable embedding capacity is maintained. The amount of distortion introduced during carrier modification is reduced. The security of information hiding is improved.

The invention discloses a blind audio watermark implementing method based on logarithmic quantization index modulation, and belongs to the technical field of audio watermarks. The blind audio watermark implementing method is characterized by including sufficiently utilizing robustness of vector norms and imperceptibility of the logarithmic quantization index modulation based on mu-law companding; improving safety and robustness of a watermark by the aid of a chaos sequence encryption watermark image; converting the sectioned vector norms of wavelet approximate components into a transform domain via the mu-law companding; then embedding an encryption binary watermark image; and extracting the binary watermark image after the watermark is attacked. The audio watermark algorithm has the advantages of high volume, fine tone, imperceptibility and robustness and low complexity, the watermark still can be correctly extracted by the aid of the method under various watermark attack conditions, and accordingly amplitude attack can be effectively resisted by the method.

The invention belongs to the technical field of digital image watermarking, and provides a robust image watermarking method and system based on deep learning and an attention network, and the method comprises the following steps: obtaining an original image tensor; obtaining an attention image tensor according to the obtained original image tensor and an attention model; generating a watermark-containing image based on the obtained attention image tensor and a watermark embedding model; generating an attacked image according to the generated watermark-containing image and the constructed attack network model; and extracting an image watermark according to the attacked image, the attention model and the deep learning model. Attention weights are deduced along two dimensions of a channel and a space by utilizing the perception capability of a human visual system to different regions and the anti-attack capability of different pixel points, and regions which have small influence on human visual perception and are good in robustness are searched for watermark embedding.

The invention discloses an MDCT quantization coefficient small value region-based advanced audio coding (AAC) audio steganography and extraction method. The method includes the following steps that: 1, a mapping table containing index pairs and code length is established according to an AAC audio codebook; steganography rules are established according to the mapping table; and 3, insertion and extraction of secret information can be realized through utilizing the mapping table and the steganography rules. With the method of the invention adopted, computational complexity in steganography and extraction processes can be decreased, and imperceptibility and anti-steganalysis of steganography can be improved, and therefore, information can be hidden in and extracted from AAC audios safely and efficiently.

The application relates to a method for hiding information and a method for obtaining information. The method for hiding the information comprises the following steps: obtaining a bit sequence at a predetermined position of a source bit-stream of public information, and initializing a complex Gauss random sequence generator by means of the bit sequence to generate a complex Gauss random sequence;performing spread spectrum modulation on secret information according to the complex Gauss random sequence to obtain a Gauss spread spectrum signal; and superimposing the Gauss spread spectrum signalon a conventional spread spectrum signal of public information to obtain a mixed signal. By means of the method for hiding the information, through pseudo-gaussianizing the communication signal of thesecret information, the mixed signal embedded in the secret information tends to be the same as the signal sent without embedding the secret information in terms of time-domain characteristic components, frequency-domain characteristic components or demodulation results; therefore, the information receiving end can recover the secret information and the public information normally, the imperceptibility of the secret information is improved, and the concealment of covert communication process is improved.

The invention relates to the technical field of image processing, and discloses a high-capacity reversible image watermarking algorithm based on a multi-scale decomposition and interpolation algorithm, which comprises a watermark embedding part and a watermark extracting part, and is characterized in that the watermark embedding capacity is further increased through multi-scale decomposition and interpolation expansion before watermark embedding; watermark information is embedded through a generalized interpolation expansion method, pixel value overflow of overflowing corresponding image pixelpoints is avoided through gray scale overflow processing, then the embedding capacity is further increased, and watermark extraction is an inverse process of watermark embedding. Compared with the prior art, the method has the advantages that when equivalent watermark information is embedded, good watermark-containing image visual quality is kept, and better invisibility and payload capacity areachieved.