# A Unified Framework for Stealthy Adversarial Generation via Latent Optimization and Transferability Enhancement Source: [A Unified Framework for Stealthy Adversarial Generation via Latent Optimization and Transferability Enhancement]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:03:44.730Z Authors: Gaozheng Pei, Ke Ma, Dongpeng Zhang, Chengzhi Sun, Qianqian Xu, Qingming Huang Published: 2025-06-30T09:59:09.000Z ## Abstract Due to their powerful image generation capabilities, diffusion-based adversarial example generation methods through image editing are rapidly gaining popularity. However, due to reliance on the discriminative capability of the diffusion model, these diffusion-based methods often struggle to generalize beyond conventional image classification tasks, such as in Deepfake detection. Moreover, traditional strategies for enhancing adversarial example transferability are challenging to adapt to these methods. To address these challenges, we propose a unified framework that seamlessly incorporates traditional transferability enhancement strategies into diffusion model-based adversarial example generation via image editing, enabling their application across a wider range of downstream tasks. Our method won first place in the "1st Adversarial Attacks on Deepfake Detectors: A Challenge in the Era of AI-Generated Media" competition at ACM MM25, which validates the effectiveness of our approach. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()