Stealthy-AE: Generating Stealthy Adversarial Examples through Online Social Networks

Deep Neural Networks (DNNs) have become increasingly prevalent in various applications, yet they remain vulnerable to adversarial attacks, particularly through the use of adversarial examples (AEs). This paper introduces the concept of stealthy AE, which is benign before transmission through Online Social Networks (OSNs) but becomes adversarial after processing. The inherent transformations applied by OSNs, such as image compression and format conversion, can activate the properties of adversarial examples that are originally hidden. We present a suite of stealthy AE generation frameworks. Subsequently, our scheme involves the quality factor calculation, leveraging the diffusion model with differential JPEG layers to simulate OSN transmission, and utilizing the Lagrange multiplier method for AE generation optimization. Extensive experiments demonstrate that our method consistently outperforms seven state-of-the-art adversarial example generation techniques across multiple OSNs and victim models. Moreover, resistance detection evaluation and extended experiments with different attack settings also demonstrated the scalability of our scheme.