Multimodal Vision Language Models (VLMs) have emerged as a transformative technology
at the intersection of computer vision and natural language processing, enabling machines …

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning
system by integrating powerful Large Language Models (LLMs) with various modality …

DeepFakes which refer to AI-generated media content have become an increasing concern
due to their use as a means for disinformation. Detecting DeepFakes is currently solved with …

Existing face forgery detection usually follows the paradigm of training models in a single
domain, which leads to limited generalization capacity when unseen scenarios and …

Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like
face forgery detection, where viewers often struggle to distinguish between real and …

The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial
advancements in artificial intelligence, significantly enhancing the capability to understand …

The generalization ability of deepfake detectors is vital for their applications in real-world
scenarios. One effective solution to enhance this ability is to train the models with manually …

The rapid advancement of deepfake technologies has sparked widespread public concern,
particularly as face forgery poses a serious threat to public information security. However …

Three key challenges hinder the development of current deepfake video detection:(1)
Temporal features can be complex and diverse: how can we identify general temporal …

With the rise of sophisticated phishing attacks, there is a growing need for effective and
economical detection solutions. This paper explores the use of large multimodal agents …