Jiansong Zhang, Kejiang Chen, Weixiang Li, Weiming Zhang, Neng H. Yu
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引用次数: 2
Abstract
The development of generative AI applications has revolutionized the data environment for steganography, providing a new source of steganographic cover. However, existing generative data-based steganography methods typically require white-box access, rendering them unsuitable for black-box generative models. To overcome this limitation, we propose a novel steganography method for generated images, which leverages the volatility of generative models and is applicable in black-box scenarios. The volatility of generative models refers to the ability to generate a series of images with slight variations by fine-tuning the input parameters of the model. These generated images exhibit varying degrees of volatility in different areas. To resist steganalysis, we mask steganographic modifications by confusing them with the inherent volatility of the model. Specifically, by modeling distributions of generated pixels and estimating the parameters of the distributions, the occurrence probabilities of generated pixels can be obtained, which serve as an effective measure for steganographic modification probabilities to render stego images as indistinguishable as possible from the images producible by the model. Moreover, we further combine it with existing costs to develop a more comprehensive steganographic algorithm. Experimental results show that the proposed method significantly outperforms baseline and comparative methods in resisting both feature-based and CNN-based steganalyzers.
期刊介绍:
The "IEEE Transactions on Dependable and Secure Computing (TDSC)" is a prestigious journal that publishes high-quality, peer-reviewed research in the field of computer science, specifically targeting the development of dependable and secure computing systems and networks. This journal is dedicated to exploring the fundamental principles, methodologies, and mechanisms that enable the design, modeling, and evaluation of systems that meet the required levels of reliability, security, and performance.
The scope of TDSC includes research on measurement, modeling, and simulation techniques that contribute to the understanding and improvement of system performance under various constraints. It also covers the foundations necessary for the joint evaluation, verification, and design of systems that balance performance, security, and dependability.
By publishing archival research results, TDSC aims to provide a valuable resource for researchers, engineers, and practitioners working in the areas of cybersecurity, fault tolerance, and system reliability. The journal's focus on cutting-edge research ensures that it remains at the forefront of advancements in the field, promoting the development of technologies that are critical for the functioning of modern, complex systems.