The fuzzy vault algorithm is mainly used for data privacy protection in fault-tolerant environments, It binds biometric features and secret data together safely to form a vault, which can realize "fuzzy" unlocking. in the real-life, fuzzy vault is mainly used in biometric authentication, image capture, recognition, etc. However, At present, most fuzzy vault schemes have the problems of high computational complexity and low communication efficiency. To solve the problems of high computational complexity and low communication efficiency in identity authentication, we based on the fuzzy vault scheme and construct a multi-secret sharing fuzzy vault scheme. which splits the big secret value into multiple sub-secret values and uses the RS code multi-secret sharing decoding method to improve the efficiency of biological identity authentication. Firstly, splits the big secret value into multiple sub-secret values. Then, We construct a multi-secret sharing scheme. our scheme supports the sharing of multi-secret information. Finally, we analyze the computational complexity and communication complexity of the scheme. The analysis shows that our scheme can reduce the computational complexity and communication complexity by an order of magnitude.
KEYWORDS: Video, Convolution, Digital watermarking, Video compression, Visualization, Steganography, Network architectures, Data modeling, Data hiding, Video processing
In robust video steganography, a message is embedded into a video such that video distortions are avoided while producing a stego video of imperceptible difference from the cover video. Traditional techniques achieved robustness against particular distortions but are complicated in computation and design, and rely on different compression standards. Nowadays, deep-learning-based methods can achieve impressive visual quality and robustness to attacks. We propose a framework with a channel-space attention mechanism for robust video steganography. The framework is composed of depthwise separable convolution layers that can learn channel-space segments for embedding and extraction. The secret messages are distributed across channel-space scales to increase imperceptibility and robustness to distortions. This end-to-end solution is trained with the 3-player game approach to conducting robust steganography, where three networks compete. Two of these handle embedding and extraction operations, while the third network simulates attacks and detection from a steganalyst as an adversarial network. Comparative results versus recent research show that our method is more robust against compression and video distortion attacks. Peak signal-to-noise ratio and structural similarity index were used for evaluating visual quality and demonstrate the imperceptibility of our method.
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