Steganographic Generative Adversarial Networks
원문 초록
Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.
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SCReedSolo: A Secure and Robust LSB Image Steganography Framework with Randomized Symmetric Encryption and Reed-Solomon Coding
Efficient Streaming Voice Steganalysis in Challenging Detection Scenarios
Natias: Neuron Attribution based Transferable Image Adversarial Steganography
Cover-separable Fixed Neural Network Steganography via Deep Generative Models
토론
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