Deepfake detectors have demonstrated vulnerability when faced with adversarial attacks. In real-world scenarios, attackers can generate adversarial examples that mislead detectors through query-based black-box attack methods. Recently, several defense methods against query-based adversarial attacks have achieved promising performance. However, their effectiveness significantly declines when applied to deepfake detection tasks. In this work, we propose a novel defense method specifically designed to counter adversarial attacks in deepfake detection scenarios. Unlike existing approaches, our method focuses on the high-frequency components of images to uncover subtle traces left by adversarial perturbations. Specifically, we analyze the high-frequency residual between similar images within the queries to detect adversarial examples. We evaluate our method against three advanced black-box attack strategies. Extensive experiments demonstrate that our approach achieves highly effective attack detection while significantly reducing FPR.

The rapid advancement of generative models has led to the widespread emergence of highly realistic synthetic images, making the detection of AI-generated content increasingly critical. In particular, diffusion models have recently achieved unprecedented levels of visual fidelity, further raising concerns. While most existing approaches rely on supervised learning, zero-shot detection methods have attracted growing interest due to their ability to bypass data collection and maintenance. Nevertheless, the performance of current zero-shot methods remains limited. In this paper, we introduce a novel zero-shot AI-generated image detection method. Unlike previous works that primarily focus on identifying artifacts in the final generated images, our work explores features within the image generation process that can be leveraged for detection. Specifically, we simulate the image sampling process via diffusion-based inversion and observe that the denoising outputs of generated images converge to the target image more rapidly than those of real images. Inspired by this observation, we compute the similarity between the original image and the outputs along the denoising trajectory, which is then used as an indicator of image authenticity.Since our method requires no training on any generated images, it avoids overfitting to specific generative models or dataset biases. Experiments across a wide range of generators demonstrate that our method achieves significant improvements over state-of-the-art supervised and zero-shot counterparts.

The security of the Universal Serial Bus (USB) is critical due to its widespread use. Attackers can exploit electromagnetic emissions from USB devices to steal sensitive information, and detecting controlled emissions from USB has long been a challenging issue. Existing methods for detecting controlled emissions based on radiated compromising emanations are often hindered by environmental noise, electromagnetic interference, and device variability, leading to reduced detection capability. To address these challenges, this paper, for the first time, proposes a method for detecting controlled emissions from USB devices through conducted compromising emanations. First, we construct a dataset of USB leakage signals based on conducted compromising emanations. Then, we propose a multiscale time-frequency processing technique for USB leakage signals, applying time-frequency analysis to generate spectrograms containing both time-domain and frequency-domain features. By leveraging multiscale data augmentation, the accuracy and generalization of detection model are enhanced. Finally, we employ a Residual Network to automatically extract features and detect abnormal signals. Extensive experiments demonstrate that this method can effectively identify controlled emissions from USB devices, achieving an AUC of 99.52% and an ACC of 96.74%, providing a robust solution for anomaly detection and enhancing the security of USB devices.