Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (9): 2925-2931.DOI: 10.11772/j.issn.1001-9081.2022081283

• Multimedia computing and computer simulation • Previous Articles     Next Articles

Image tampering forensics network based on residual feedback and self-attention

Guolong YUAN, Yujin ZHANG(), Yang LIU   

  1. School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2022-08-28 Revised:2022-10-31 Accepted:2022-11-03 Online:2023-01-11 Published:2023-09-10
  • Contact: Yujin ZHANG
  • About author:YUAN Guolong, born in 1997, M. S. candidate. His research interests include image processing, image tampering forensics.
    LIU Yang, born in 1998, M. S. candidate. His research interests include image resampling forensics.
  • Supported by:
    Natural Science Foundation of Shanghai(17ZR1411900)


袁国龙, 张玉金(), 刘洋   

  1. 上海工程技术大学 电子电气工程学院,上海 201620
  • 通讯作者: 张玉金
  • 作者简介:袁国龙(1997—),男,安徽阜阳人,硕士研究生,CCF会员,主要研究方向:图像处理、图像篡改取证
  • 基金资助:


The existing multi-tampering type image forgery detection algorithms using noise features often can not effectively detect the feature difference between tampered areas and non-tampered areas, especially for copy-move tampering type. To this end, a dual-stream image tampering forensics network fusing residual feedback and self-attention mechanism was proposed to detect tampering artifacts such as unnatural edges of RGB pixels and local noise inconsistence respectively through two streams. Firstly, in the encoder stage, multiple dual residual units integrating residual feedback were used to extract relevant tampering features to obtain coarse feature maps. Secondly, further feature reinforcement was performed on the coarse feature maps by the improved self-attention mechanism. Thirdly, the mutual corresponding shallow features of encoder and deep features of decoder were fused. Finally, the final features of tempering extracted by the two streams were fused in series, and then the pixel-level localization of the tampered area was realized through a special convolution operation. Experimental results show that the F1 score and Area Under Curve (AUC) value of the proposed network on COVERAGE dataset are better than those of the comparison networks. The F1 score of the proposed network is 9.8 and 7.7 percentage points higher than that of TED-Net (Two-stream Encoder-Decoder Network) on NIST16 and Columbia datasets, and the AUC increases by 1.1 and 6.5 percentage points, respectively. The proposed network achieves good results in copy-move tampering type detection, and is also suitable for other tampering type detection. At the same time, the proposed network can locate the tampered area at pixel level accurately, and its detection performance is superior to the comparison networks.

Key words: image tampering, encoder-decoder, feature reinforcement, residual feedback, self-attention mechanism, noise feature


现存的使用噪声特征的多篡改类型图像伪造检测算法,往往不能有效地检测篡改区域和非篡改区域之间的特征差异,特别是对复制-粘贴篡改类型。为此,提出一种融合残差反馈和自注意力机制的双流编-解码器图像篡改取证网络,通过两个流分别检测RGB像素的非自然边缘等篡改伪影和局部噪声不一致性。首先,在编码器阶段使用多个融合残差反馈的双重残差单元提取相关篡改特征,以获得粗特征图;其次,通过改进后的自注意力机制对粗特征图进行进一步特征增强;随后,将互相对应的编码器浅层特征和解码器深层特征进行融合;最后,串联融合两个流最终提取到的篡改特征,再通过一个特殊卷积操作实现对篡改区域的像素级定位。实验结果表明,所提网络在COVERAGE数据集上的F1值和曲线下面积(AUC)优于对比网络。在NIST16、Columbia数据集上,所提网络的F1值相较于TED-Net(Two-stream Encoder-Decoder Network)分别提高了9.8和7.7个百分点,AUC分别提高了1.1和6.5个百分点。所提网络在复制-粘贴篡改类型检测上取得了良好的效果,并且也适用于其他篡改类型检测。同时,该网络能在像素级上对篡改区域准确定位,检测性能优于对比网络。

关键词: 图像篡改, 编-解码器, 特征增强, 残差反馈, 自注意力机制, 噪声特征

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