Exploring Symmetry in Digital Image Forensics Using a Lightweight Deep-Learning Hybrid Model for Multiple Smoothing Operators

Abstract

Digital images are widely used for informal information sharing, but the rise of fake photos spreading misinformation has raised concerns. To address this challenge, image forensics is employed to verify the authenticity and trustworthiness of these images. In this paper, an efficient scheme for detecting commonly used image smoothing operators is presented while maintaining symmetry. A new lightweight deep-learning network is proposed, which is trained with three different optimizers to avoid downsizing to retain critical information. Features are extracted from the activation function of the global average pooling layer in three trained deep networks. These extracted features are then used to train a classification model with an SVM classifier, resulting in significant performance improvements. The proposed scheme is applied to identify averaging, Gaussian, and median filtering with various kernel sizes in small-size images. Experimental analysis is conducted on both uncompressed and JPEG-compressed images, showing superior performance compared to existing methods. Notably, there are substantial improvements in detection accuracy, particularly by 6.50% and 8.20% for 32 × 32 and 64 × 64 images when subjected to JPEG compression at a quality factor of 70.