Zhu Meilong, Li Mingda, Wang Zhaohui
China Telecom Research Institute, Beijing, 102209, China.
Sci Rep. 2024 Oct 30;14(1):26166. doi: 10.1038/s41598-024-76388-9.
Malicious image tampering has gradually become another way to threaten social stability and personal safety. Timely detection and precise positioning can help reduce the occurrence of risks and improve the overall safety of society. Due to the limitations of highly targeted dataset training and low-level feature extraction efficiency, the generalization and actual performance of the recent tampered detection technology have not yet reached expectations. In this study, we propose a tampered image detection method based on RDS-YOLOv5 feature enhancement transformation. Firstly, a multi-channel feature enhancement fusion algorithm is proposed to enhance the tampering traces in tampered images. Then, an improved deep learning model named RDS-YOLOv5 is proposed for the recognition of tampered images, and a nonlinear loss metric of aspect ratio was introduced into the original SIOU loss function to better optimize the training process of the model. Finally, RDS-YOLOv5 is trained by combining the features of the original image and the enhancement image to improve the robustness of the detection model. A total of 6187 images containing three forms of tampering: splice, remove, and copy-move were used to comprehensively evaluate the proposed algorithm. In ablation test, compared with the original YOLOv5 model, RDS-YOLOv5 achieved a performance improvement of 6.46%, 5.13%, and 3.15% on F1-Score, mAP50 and mAP95, respectively. In comparative experiments, using SRIOU as the loss function significantly improved the model's ability to search for the real tampered regions by 2.54%. And the RDS-YOLOv5 model trained by the fusion dataset further improved the comprehensive detection performance by about 1%.
恶意图像篡改已逐渐成为威胁社会稳定和人身安全的另一种方式。及时检测和精确定位有助于降低风险发生，提高社会整体安全性。由于高针对性数据集训练的局限性和低级特征提取效率低下，近期的篡改检测技术的泛化能力和实际性能尚未达到预期。在本研究中，我们提出了一种基于RDS-YOLOv5特征增强变换的篡改图像检测方法。首先，提出了一种多通道特征增强融合算法，以增强篡改图像中的篡改痕迹。然后，提出了一种名为RDS-YOLOv5的改进深度学习模型用于篡改图像识别，并将长宽比的非线性损失度量引入原始SIOU损失函数中，以更好地优化模型的训练过程。最后，结合原始图像和增强图像的特征对RDS-YOLOv5进行训练，以提高检测模型的鲁棒性。总共使用了6187张包含拼接、删除和复制-移动三种篡改形式的图像来全面评估所提出的算法。在消融测试中，与原始YOLOv5模型相比，RDS-YOLOv5在F1分数、mAP50和mAP95上的性能分别提高了6.46%、5.13%和3.15%。在对比实验中，使用SRIOU作为损失函数显著提高了模型搜索真实篡改区域的能力2.54%。并且由融合数据集训练的RDS-YOLOv5模型进一步将综合检测性能提高了约1%。
