Detection of malicious spatial-domain steganography over noisy channels using convolutional neural networks

Swaroop Shankar Prasad, Ofer Hadar, Ilia Polian

Research output: Contribution to journalConference articlepeer-review


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 network-based 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.

Original languageAmerican English
Article number076
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Issue number4
StatePublished - 26 Jan 2020
Event2020 Media Watermarking, Security, and Forensics Conference, MWSF 2020 - Burlingame, United States
Duration: 26 Jan 202030 Jan 2020


  • Convolutional Neural networks
  • Deep learning
  • Image steganography
  • Malicious steganography
  • Steganalysis
  • Transmission over noisy channels

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics


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