Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compression for color images is vital in the field of image forensics. In previous researches, there have been various approaches to detecting double JPEG compression with different quantization matrices. However, the detection of double JPEG color images with the same quantization matrix is still a challenging task. An effective detection approach to extract features is proposed in this paper by combining traditional analysis with Convolutional Neural Networks (CNN). On the one hand, the number of nonzero pixels and the sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the rounding error, the truncation error and the quantization coefficient matrix are used to generate a total of 128-dimensional features via a specially designed CNN. In such a CNN, convolutional layers with fixed kernel of $1\times 1$ and Dropout layers are adopted to prevent overfitting of the model, and an average pooling layer is used to extract local characteristics. In this approach, the Support Vector Machine (SVM) classifier is applied to distinguish whether a given color image is primarily or secondarily compressed. The approach is also suitable for the case when customized needs are considered. The experimental results show that the proposed approach is more effective than some existing ones when the compression quality factors are low.