Year: 2025
Type: article
Abstract: Adversarial examples pose severe challenges to information security, as their impacts directly extend to steganography and steganalysis technologies. This scenario, in turn, has further spurred the research and application of adversarial steganography. In response, we propose a novel adversarial embedding scheme rooted in a hybrid, partially data-driven approach. The proposed scheme first leverages an adversarial neural network (UT-GAN, Universal Transform Generative Adversarial Network) to generate stego images as a preprocessing step. Subsequently, it dynamically adjusts the cost function with the aid of a DCTR (Discrete Cosine Transform Residual)-based gradient calculator to optimize the images, ensuring that the final adversarial images can resist detection by steganalysis tools. The encoder in this scheme adopts a unique architecture, where its internal parameters are determined by a partially data-driven mechanism. This design not only enhances the capability of traditional steganography schemes to counter advanced steganalysis technologies but also effectively reduces the computational overhead during stego image generation.
Source: Electronics
Institution: Hunan University of Technology and Business
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415208381