2019/01/30 by Dahuin Jung, Ho Bae, Jung, Dahuin +5
Computer Science · #Advanced Steganography and Watermarking Techniques #Chaos-based Image/Signal Encryption #Cryptography and Security (cs.CR) #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Multimedia (cs.MM) #cs.CR #cs.LG #cs.MM
paper · pdf · doi:10.48550/arxiv.1902.11113
The updated version of this paper is uploaded in arXiv:1902.10905 as a revised title. Sorry for inconvenience
openalex publication_date 2019/01/30 · arxiv created 2019/03/02 · arxiv updated 2019/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego image. Steganography is commonly used for illegal purposes such as terrorist activities and pornography. To thwart covert communications and transactions, attacking algorithms against steganography, called steganalysis, exist. Currently, there are many studies implementing deep learning to the steganography algorithm. However, conventional steganalysis is no longer effective for deep learning based steganography algorithms. Our framework is the first one to disturb covert communications and transactions via the recent deep learning-based steganography algorithms. We first extract a sophisticated pixel distribution of the potential stego image from the auto-regressive model induced by deep learning. Using the extracted pixel distributions, we detect whether an image is the stego or not at the pixel level. Each pixel value is adjusted as required and the adjustment induces an effective removal of the secret image. Because the decoding method of deep learning-based steganography algorithms is approximate (lossy), which is different from the conventional steganography, we propose a new quantitative metric that is more suitable for measuring the accurate effect. We evaluate our method using three public benchmarks in comparison with a conventional steganalysis method and show up to a 20% improvement in terms of decoding rate.