基于局部纹理差异特征增强的Deepfake检测方法  

Deepfake Detection Based on Local Texture Difference Feature Enhancement

作　　者：韦争争 WEI Zhengzheng(School of Computer Science and Engineering,Anhui University of Science and Technology,Anhui Huainan 232001,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001

出　　处：《重庆工商大学学报(自然科学版)》2025年第2期78-85,共8页Journal of Chongqing Technology and Business University:Natural Science Edition

摘　　要：目的针对当前Deepfake检测侧重全局伪造特征,而局部纹理差异特征利用不足导致模型泛化性能差的问题,提出一种基于局部纹理差异特征增强的Deepfake检测模型,通过挖掘伪造图像内在的空间伪造模式,提高检测的准确性和泛化性。方法模型首先通过中心差分卷积操作捕捉像素强度和像素梯度两种信息,从而获得更精确的局部纹理差异信息,提高对伪造图像的敏感性。其次,构建双层注意力模块,旨在利用空间注意力学习位置敏感的权重信息,并通过通道注意力自适应调整通道重要性,定位重要纹理差异特征的位置,增强纹理差异特征的表示。结果在高质量和低质量的FaceForensics++数据集上的实验,平均准确率分别达到了97.36%和92.37%,而Celeb-DF数据集上的跨数据集实验获得了比当前先进的检测模型更好的泛化性,大量的消融实验表明了方法的有效性。结论实验表明:引入中心差分和双层注意力模块后模型能够更好地捕捉图像的纹理差异信息,适应不同场景和压缩率的伪造检测,有效提高了Deepfake检测的准确性和泛化性。Objective Current Deepfake detection methods primarily focus on global forgery features,leading to poor generalization performance of the model due to insufficient utilization of local texture contrast features.To address this issue,a Deepfake detection model based on local texture difference feature enhancement was proposed,aiming to improve detection accuracy and generalization by exploring intrinsic spatial forgery patterns in forged images.Methods Firstly,the model captured both pixel intensity and pixel gradient by center difference convolution operation,to obtain more accurate local texture difference information and improve the sensitivity to forged images.Secondly,a dual-layer attention module was constructed,aiming to use spatial attention to learn location-sensitive weighting information and adaptively adjust the channel importance through channel attention to locate the position of important texture disparity features and enhance the representation of texture disparity features.Results Experiments on high-quality and low-quality FaceForensics++datasets obtained average accuracies of 97.36%and 92.37%,respectively,while cross-dataset experiments on the Celeb-DF dataset obtained better generalization performance than current state-of-the-art detection models.Extensive ablation studies validate the effectiveness of the proposed method.Conclusion Experiments show that integrating center difference convolution and a dual-layer attention module enables the model to better capture texture difference information in images,adapt to different scenarios and compression rates in forgery detection,and effectively improve the accuracy and generalization of Deepfake detection.

关 键 词：Deepfake检测 纹理差异 中心差分卷积 空间注意力 通道注意力 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]