A customized CNN architecture for detecting double and multiple JPEG compression in small blocks

Abstract

Detecting double JPEG compression is crucial for verifying the authenticity and integrity of JPEG images. However, this poses significant challenges, particularly when the quality factor (QF) of the first compression is substantially higher than of the second compression, and when working with small-sized blocks. To overcome these challenges, we propose a customized convolutional neural network (CNN) architecture specifically designed for detecting double and multiple JPEG compression. The main contribution of our work is the innovative use of raw DCT coefficients as input to the CNN, enabling end-to-end feature extraction and enhancing the detection of subtle compression artifacts that might be missed by traditional techniques. The framework incorporates an ABS layer after the first convolution to eliminate the symmetry information that are not useful for JPEG compression detection, applies batch normalization (BN) to stabilize training, and reduces model complexity by employing 1 × 1 convolutions in deeper layers. Experimental results show that the proposed customized architecture significantly improves detection accuracy compared to other state-of-the-art methods, particularly in challenging small-block scenarios, and demonstrates effectiveness in detecting both double and multiple JPEG compression.