DOI: 10.57760/sciencedb.j00240.00067
title: Large-scale Chinese data evaluation benchmark for face video forgery detection
title: 面向人脸视频防伪检测的大规模中文数据测评基准

description: Purpose: To address the deceptive issue of highly realistic fake facial videos generated by AIGC technology on human visual perception, as well as the current face anti-counterfeiting detection The algorithm evaluation system has gaps in the effectiveness and applicability verification of Chinese data, aiming to build a quantitative evaluation benchmark for Chinese scenarios to promote the iterative development of anti-counterfeiting detection technology. The method proposes a CHN-DF dataset for large-scale Chinese facial forgery videos, detailing the entire process of data collection, forgery sample generation, and quality assessment. Verify the complexity of the dataset through multidimensional experiments, taking into account complex factors such as cross modal forgery technology coverage and environmental interference factor completeness, and establish a systematic evaluation benchmark based on deep detection models. The world's first Chinese facial video anti-counterfeiting dataset containing 434727 samples was released as a result. The experiment showed that the dataset had high difficulty in identification, and the accuracy of visual and audio-visual combination was controlled below 85% and 70% in 16 evaluations including SOTA and mainstream anti-counterfeiting models, respectively. The constructed evaluation benchmark covers both visual and auditory modal scenes, and shows an average fluctuation of 19% in model accuracy performance in cross domain generalization testing 6%, significantly revealing the application limitations of existing algorithms. Conclusion: The constructed Chinese anti-counterfeiting evaluation benchmark effectively fills the gap in the field. Through systematic experiments, the correlation mechanism between dataset characteristics and algorithm performance is elucidated, and key development directions such as enhancing model robustness and cross modal generalization ability are proposed. This provides data support and practical guidance for quantitative evaluation in Chinese scenarios and the actual deployment of facial video anti-counterfeiting technology. 
description: Purpose: To address the deceptive issue of highly realistic fake facial videos generated by AIGC technology on human visual perception, as well as the current face anti-counterfeiting detection The algorithm evaluation system has gaps in the effectiveness and applicability verification of Chinese data, aiming to build a quantitative evaluation benchmark for Chinese scenarios to promote the iterative development of anti-counterfeiting detection technology. The method proposes a CHN-DF dataset for large-scale Chinese facial forgery videos, detailing the entire process of data collection, forgery sample generation, and quality assessment. Verify the complexity of the dataset through multidimensional experiments, taking into account complex factors such as cross modal forgery technology coverage and environmental interference factor completeness, and establish a systematic evaluation benchmark based on deep detection models. The world's first Chinese facial video anti-counterfeiting dataset containing 434727 samples was released as a result. The experiment showed that the dataset had high difficulty in identification, and the accuracy of visual and audio-visual combination was controlled below 85% and 70% in 16 evaluations including SOTA and mainstream anti-counterfeiting models, respectively. The constructed evaluation benchmark covers both visual and auditory modal scenes, and shows an average fluctuation of 19% in model accuracy performance in cross domain generalization testing 6%, significantly revealing the application limitations of existing algorithms. Conclusion: The constructed Chinese anti-counterfeiting evaluation benchmark effectively fills the gap in the field. Through systematic experiments, the correlation mechanism between dataset characteristics and algorithm performance is elucidated, and key development directions such as enhancing model robustness and cross modal generalization ability are proposed. This provides data support and practical guidance for quantitative evaluation in Chinese scenarios and the actual deployment of facial video anti-counterfeiting technology.