Detecting Double Jpeg Compression with Same Quantization Matrix Based on Dense Cnn Feature
Xiaosa Huang, Shilin Wang, Gongshen Liu · 2018
Detection of double JPEG compression with same quantization matrix has been regarded as a challenging task in digital image forensics because there are very few modification cues in the tampered images especially when the compression quality factor is low. In order to solve this problem, a comprehensive feature representation based on the dense CNN framework is proposed, which is sensitive to the artifacts caused by double JPEG compression and is not related to the image content. With the appropriate network design and contributing to the characteristics of average pooling, dense connection and transition, the proposed network can differentiate double JPEG compression artifacts accurately. Experiment results on the two datasets have demonstrated that the proposed feature outperforms several state-of-the-art approaches investigated.