mlsanityData to 14 Oct 2026

Deniable Steganography

arXiv 2205.12587 ↗First posted 25 May 2022cs.CR cs.CVcited by 0

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Abstract (arXiv)

Steganography conceals the secret message into the cover media, generating a stego media which can be transmitted on public channels without drawing suspicion. As its countermeasure, steganalysis mainly aims to detect whether the secret message is hidden in a given media. Although the steganography techniques are improving constantly, the sophisticated steganalysis can always break a known steganographic method to some extent. With a stego media discovered, the adversary could find out the sender or receiver and coerce them to disclose the secret message, which we name as coercive attack in this paper. Inspired by the idea of deniable encryption, we build up the concepts of deniable steganography for the first time and discuss the feasible constructions for it. As an example, we propose a receiver-deniable steganographic scheme to deal with the receiver-side coercive attack using deep neural networks (DNN). Specifically, besides the real secret message, a piece of fake message is also embedded into the cover. On the receiver side, the real message can be extracted with an extraction module; while once the receiver has to surrender a piece of secret message under coercive attack, he can extract the fake message to deceive the adversary with another extraction module. Experiments demonstrate the scalability and sensitivity of the DNN-based receiver-deniable steganographic scheme.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 25 May 2022.

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18 of 52 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2016Densely Connected Convolutional Networkscited by 6,720
  6. 2022Generative Adversarial Networkscited by 5,627
  7. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  8. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  9. 2018HiDDeN: Hiding Data With Deep Networkscited by 397
  10. 2019StegaStamp: Invisible Hyperlinks in Physical Photographscited by 265
  11. 2019SteganoGAN: High Capacity Image Steganography with GANscited by 72
  12. 2017Generating Steganographic Images via Adversarial Trainingcited by 58
6 more
  1. 2017Steganographic Generative Adversarial Networkscited by 21
  2. 2017SSGAN: Secure Steganography Based on Generative Adversarial Networkscited by 18
  3. 2017End-to-end Trained CNN Encode-Decoder Networks for Image Steganographycited by 18
  4. 2018StegNet: Mega Image Steganography Capacity with Deep Convolutional Networkcited by 14
  5. 2017Unsupervised Steganalysis Based on Artificial Training Setscited by 5
  6. 2018Spatial Image Steganography Based on Generative Adversarial Networkcited by 4

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