arXiv subjects

Zonghui Guo

Publications and source records attributed to Zonghui Guo.

2 recordsLinked to original sources

arXiv2025

REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models

The rapid evolution of Large Vision-Language Models (LVLMs) has highlighted the necessity for comprehensive evaluation frameworks that assess these models across diverse dimensions. While existing benchmarks focus on specific aspects such as perceptual abilities, cognitive capabilities, and safety against adversarial attacks, they often lack the breadth and depth required to provide a holistic understanding of LVLMs' strengths and limitations. To address this gap, we introduce REVAL, a comprehensive benchmark designed to evaluate the \textbf{RE}liability and \textbf{VAL}ue of LVLMs. REVAL encompasses over 144K image-text Visual Question Answering (VQA) samples, structured into two primary sections: Reliability, which assesses truthfulness (\eg, perceptual accuracy and hallucination tendencies) and robustness (\eg, resilience to adversarial attacks, typographic attacks, and image corruption), and Values, which evaluates ethical concerns (\eg, bias and moral understanding), safety issues (\eg, toxicity and jailbreak vulnerabilities), and privacy problems (\eg, privacy awareness and privacy leakage). We evaluate 26 models, including mainstream open-source LVLMs and prominent closed-source models like GPT-4o and Gemini-1.5-Pro. Our findings reveal that while current LVLMs excel in perceptual tasks and toxicity avoidance, they exhibit significant vulnerabilities in adversarial scenarios, privacy preservation, and ethical reasoning. These insights underscore critical areas for future improvements, guiding the development of more secure, reliable, and ethically aligned LVLMs. REVAL provides a robust framework for researchers to systematically assess and compare LVLMs, fostering advancements in the field.

cs.CV↗

arXiv2024

Revisiting Face Forgery Detection: From Facial Representation to Forgery Detection

Face Forgery Detection (FFD), or Deepfake detection, aims to determine whether a digital face is real or fake. Due to different face synthesis algorithms with diverse forgery patterns, FFD models often overfit specific patterns in training datasets, resulting in poor generalization to other unseen forgeries. Existing FFD methods primarily leverage pre-trained backbones with general image representation capabilities and fine-tune them to identify facial forgery cues. However, these backbones lack domain-specific facial knowledge and insufficiently capture complex facial features, thus hindering effective implicit forgery cue identification and limiting generalization. Therefore, it is essential to revisit FFD workflow across the \textit{pre-training} and \textit{fine-tuning} stages, achieving an elaborate integration from facial representation to forgery detection to improve generalization. Specifically, we develop an FFD-specific pre-trained backbone with superior facial representation capabilities through self-supervised pre-training on real faces. We then propose a competitive fine-tuning framework that stimulates the backbone to identify implicit forgery cues through a competitive learning mechanism. Moreover, we devise a threshold optimization mechanism that utilizes prediction confidence to improve the inference reliability. Comprehensive experiments demonstrate that our method achieves excellent performance in FFD and extra face-related tasks, \ie, presentation attack detection. Code and models are available at \href{https://github.com/zhenglab/FFDBackbone}{https://github.com/zhenglab/FFDBackbone}.

cs.CV↗