基于特征融合的篡改与深度伪造图像检测算法

Detection Algorithm of Tamper and Deepfake Image Based on Feature Fusion

如今,恶意篡改与深度伪造图片的数量呈现爆发性增长态势,而现有图像篡改检测方法普遍存在适用范围有限、检测准确率不高等问题。针对此类问题,文章提出了一种基于图像纹理特征的篡改与伪造图像分类检测算法,首次将Cb与Cr通道经过Scharr算子提取的一阶梯度边缘纹理图片与G通道经过Laplacian算子提取的二阶梯度边缘纹理图片结合,使用灰度共生矩阵(GLCM)融合并提取图片的纹理特征,最后经过EfficientNet进行篡改与深度伪造监测。通过在各类图像篡改与深度伪造数据集上的实验,验证了该模型在两类二分类检测任务上都具有广泛的适用性与高检测准确率,对于多种深度伪造人脸算法所生成图片的分类检测准确率均能达到99.9%。

Nowadays,malicious tampering and forgery images show an explosive growth trend.The existing image tampering detection methods generally have the problems of single application scope and low detection accuracy.To solve these problems,this paper proposes a tampering and forgery image classification detection network based on image texture features.For the first time,it combines the first step edge texture image of Cb and Cr channel through Scharr operator with the second step edge texture image of G channel through Laplacian operator.Gray Level Co-occurrence Matrix(GLCM)is used to extract the features of texture image.Finally,the tampering and forgery are monitored by EfficientNet.Experiments on various image tampering and deep forgery datasets show that the model has wide applicability and high detection accuracy in both types of detection,and the classification detection accuracy of images generated by various Deepfake algorithms can reach 99.9%.