# Towards Deep Network Steganography: From Networks to Networks

arXiv: https://arxiv.org/abs/2307.03444 · HTML: https://mlsanity.com/paper/2307.03444/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Detecting AI Trojans Using Meta Neural Analysis](https://mlsanity.com/paper/1910.03137/) (2019)
- [Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning](https://mlsanity.com/paper/2004.11627/) (2020)
- [Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication](https://mlsanity.com/paper/2104.04268/) (2021)
- [Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services](https://mlsanity.com/paper/2105.12344/) (2021)
- [DeepiSign: Invisible Fragile Watermark to Protect the Integrityand Authenticity of CNN](https://mlsanity.com/paper/2101.04319/) (2021)

## Built on by (most cited first, 1 of 1)

- [StegaINR4MIH: steganography by implicit neural representation for multi-image hiding](https://mlsanity.com/paper/2410.10117/) (2024)

## Abstract (arXiv)

With the widespread applications of the deep neural network (DNN), how to covertly transmit the DNN models in public channels brings us the attention, especially for those trained for secret-learning tasks. In this paper, we propose deep network steganography for the covert communication of DNN models. Unlike the existing steganography schemes which focus on the subtle modification of the cover data to accommodate the secrets, our scheme is learning task oriented, where the learning task of the secret DNN model (termed as secret-learning task) is disguised into another ordinary learning task conducted in a stego DNN model (termed as stego-learning task). To this end, we propose a gradient-based filter insertion scheme to insert interference filters into the important positions in the secret DNN model to form a stego DNN model. These positions are then embedded into the stego DNN model using a key by side information hiding. Finally, we activate the interference filters by a partial optimization strategy, such that the generated stego DNN model works on the stego-learning task. We conduct the experiments on both the intra-task steganography and inter-task steganography (i.e., the secret and stego-learning tasks belong to the same and different categories), both of which demonstrate the effectiveness of our proposed method for covert communication of DNN models.