脆弱的重建：基于重建的检测器在扩散生成图像中的对抗脆弱性
Fragile Reconstruction: Adversarial Vulnerability of Reconstruction-Based Detectors for Diffusion-Generated Images
AI总结：
研究发现基于重建的检测器在扩散生成图像检测中存在严重对抗脆弱性，通过微小对抗扰动可使检测准确率降至近零，揭示了现有检测策略的安全隐患。
摘要：
最近，检测由扩散模型生成的AI图像因其对安全的潜在威胁而受到越来越多关注。在现有方法中，基于重建的方法已成为该任务的重要范式。然而，我们发现此类方法对对抗扰动存在严重安全漏洞；即通过向输入图像添加不可察觉的对抗扰动，分类器的检测准确率会降至近零。为了验证这一威胁，我们系统评估了三个代表性检测器在四个不同生成基础模型上的对抗鲁棒性。首先，我们构建了白盒场景中的对抗攻击，这些攻击会降低所有经过良好训练的检测器的性能。此外，我们发现这些攻击具有可转移性；即针对一个检测器设计的攻击可以转移到其他检测器上，表明对抗攻击在黑盒设置下也可以被构造。最后，我们评估了常见的防御措施，并发现标准对抗攻击防御方法提供的缓解有限。我们将这些失败归因于检测器感知到的受攻击样本的信噪比（SNR）较低。总体而言，我们的结果揭示了基于重建的检测器的根本安全限制，并突显了重新思考现有检测策略的必要性。
英文摘要：
Recently, detecting AI-generated images produced by diffusion-based models has attracted increasing attention due to their potential threat to safety. Among existing approaches, reconstruction-based methods have emerged as a prominent paradigm for this task. However, we find that such methods exhibit severe security vulnerabilities to adversarial perturbations; that is, by adding imperceptible adversarial perturbations to input images, the detection accuracy of classifiers collapses to near zero. To verify this threat, we present a systematic evaluation of the adversarial robustness of three representative detectors across four diverse generative backbone models. First, we construct adversarial attacks in white-box scenarios, which degrade the performance of all well-trained detectors. Moreover, we find that these attacks demonstrate transferability; specifically, attacks crafted against one detector can be transferred to others, indicating that adversarial attacks on detectors can also be constructed in a black-box setting. Finally, we assess common countermeasures and find that standard defense methods against adversarial attacks provide limited mitigation. We attribute these failures to the low signal-to-noise ratio (SNR) of attacked samples as perceived by the detectors. Overall, our results reveal fundamental security limitations of reconstruction-based detectors and highlight the need to rethink existing detection strategies.
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