11 accepted papers
Generative models now produce imperceptible, fine-grained manipulated faces, posing significant privacy risks. However, existing AI-generated face datasets generally lack focus on samples with fine-grained regional manipulations. Furthermore, no researchers have yet studied the real impact of splice…
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…
Exploring multimodal information from external knowledge bases in Visual Question Answering (VQA) reasoning tasks presents a significant challenge. Current methods, such as attention-based and graph-based approaches, have limitations in effectively capturing contextual key information. It is essenti…
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…
Cross-modal retrieval with deep neural networks heavily relies on accurate annotation. However, existing methods may easily suffer from the scarcity and validity of annotations due to the expensive cost of manual labeling. In addition, it is inevitable that noisy labels are imposed during labeling.…
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…
DeepFake, an AI technology for creating facial forgeries, has garnered global attention. Amid such circumstances, forensics researchers focus on developing defensive algorithms to counter these threats. In contrast, there are techniques developed for enhancing the aggressiveness of DeepFake, e.g., t…
In recent years, the task of multi-view clustering(MVC) has attracted more and more attention. Meanwhile, the graph convolution network(GCN) based MVC method has made consistent achievements in processing graph-structured data. However, real world data often suffers from missing some instances in ea…
Although Vision transformers (ViTs) have recently dominated many vision tasks, deploying ViT models on resource-limited devices remains a challenging problem. To address such a challenge, several methods have been proposed to compress ViTs. Most of them borrow experience in convolutional neural netw…