Natias: Neuron Attribution based Transferable Image Adversarial Steganography

• URL: http://arxiv.org/abs/2409.04968v1
• Date: Sun, 8 Sep 2024 04:09:51 GMT
• Title: Natias: Neuron Attribution based Transferable Image Adversarial Steganography
• Authors: Zexin Fan, Kejiang Chen, Kai Zeng, Jiansong Zhang, Weiming Zhang, Nenghai Yu,
• Abstract summary: adversarial steganography has garnered considerable attention due to its ability to effectively deceive deep-learning-based steganalysis. We propose a novel adversarial steganographic scheme named Natias. Our proposed method can be seamlessly integrated with existing adversarial steganography frameworks.
• Score: 62.906821876314275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image steganography is a technique to conceal secret messages within digital images. Steganalysis, on the contrary, aims to detect the presence of secret messages within images. Recently, deep-learning-based steganalysis methods have achieved excellent detection performance. As a countermeasure, adversarial steganography has garnered considerable attention due to its ability to effectively deceive deep-learning-based steganalysis. However, steganalysts often employ unknown steganalytic models for detection. Therefore, the ability of adversarial steganography to deceive non-target steganalytic models, known as transferability, becomes especially important. Nevertheless, existing adversarial steganographic methods do not consider how to enhance transferability. To address this issue, we propose a novel adversarial steganographic scheme named Natias. Specifically, we first attribute the output of a steganalytic model to each neuron in the target middle layer to identify critical features. Next, we corrupt these critical features that may be adopted by diverse steganalytic models. Consequently, it can promote the transferability of adversarial steganography. Our proposed method can be seamlessly integrated with existing adversarial steganography frameworks. Thorough experimental analyses affirm that our proposed technique possesses improved transferability when contrasted with former approaches, and it attains heightened security in retraining scenarios.

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