摘要
随着深度伪造技术的发展,生成的图片视频质量越来越逼真,给社会带来了巨大的安全风险。针对现有的检测方法参数量大、网络较深、模型结构复杂等情况,首先对取证领域的经典检测模型XceptionNet进行优化,提出一种轻量化的取证模型Xcep_Block8,在减少模型参数量的同时,仍保持较高的检测精度。其次,针对类别不均衡问题,通过提高较少类别样本的采样概率,较好地解决了正负样本不均的情况。最后使用混合式数据增强方法CutMix增强样本之间的线性表达。实验结果表明,所提模型的测试结果较基线结果提升约1.01个百分点,同时在参数量方面较其他方法也有一定优势。
The rapid development of deep forgery technology has improved the quality of generated pictures and videos to mirror reality.However,it has brought huge security risks to society.In view of the large parameters used in existing detection methods,deep network,complex model structure,etc.,this paper first optimizes the classic detection model XceptionNet in the forensics field and proposes a lightweight forensic model Xcep_Block8 that reduces the model parameters while maintaining high detection accuracy.Second,we improve the solution of the unevenness of positive and negative samples by increasing the sampling probability of samples with fewer categories to solve the problem of unbalanced categories.Finally,we employ the hybrid data enhancement method,CutMix,to improve the linear expression between samples.The experimental results show that the test results of the proposed model are about 1.01percentage points higher than the baseline results.Additionally,it has certain advantages compared with other methods in terms of parameter quantity.
作者
耿鹏志
唐云祁
樊红兴
朱新同
Geng Pengzhi;Tang Yunqi;Fan Hongxing;Zhu Xintong(School of Criminal Investigation,People’s Public Security University of China,Beijing 100038,China;Center for Research on Intelligent Perception and Computing,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第16期348-355,共8页
Laser & Optoelectronics Progress
基金
中央高校基本科研业务费项目(2021JKF203)
上海市现场物证重点实验室开放课题基金(2021XCWZK04)。
