Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are
transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible
even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise
critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with
a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.
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Convolutional Neural Networks;
Transmission over noisy channels
Document Type: Research Article
January 26, 2020
This article was made available online on January 26, 2020 as a Fast Track article with title: "Detection of malicious spatial-domain steganography over noisy channels using convolutional neural networks".
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