6 accepted papers
Fairness is a core element in the trustworthy deployment of deepfake detection models, especially in the field of digital identity security. Biases in detection models toward different demographic groups, such as gender and race, may lead to systemic misjudgments, exacerbating the digital divide and
Current AIGC detectors often achieve near-perfect accuracy on images produced by the same generator used for training but struggle to generalize to outputs from unseen generators. We trace this failure in part to latent prior bias: detectors learn shortcuts tied to patterns stemming from the initial…
The high-quality, realistic images generated by generative models pose significant challenges for exposing them. So far, data-driven deep neural networks have been justified as the most efficient forensics tools for the challenges. However, they may be over-fitted to certain semantics, resulting in…
Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only yield a binary decision, incapable of forgery localization, attribution of forgery methods, and providing analysis on th…
Although effective deepfake detection models have been developed in recent years recent studies have revealed that these models can result in unfair performance disparities among demographic groups such as race and gender. This can lead to particular groups facing unfair targeting or exclusion from…
In recent years, a number of lightweight single-image super-resolution (SISR) network methods heave been proposed. However, most existing approaches do not make full use of the information before and after the convolution and the high-frequency information of the image. In this paper, we propose a l…