Combining Deep Learning and Forensic Analysis for Robust Fake Image Detection
Sonia Yadav -, A. V. Ravi Kumar · International Journal on Science and Technology · 2025
The increasing sophistication of image editing tools and AI-generated content has led to a surge in the dissemination of fake images across digital platforms.This study proposes a hybrid method for detecting fake images by integrating Convolutional Neural Networks (CNNs) with conventional image forensic strategies, leveraging the strengths of both approaches.CNNs are employed to detect pixel-level inconsistencies and complex patterns introduced during image manipulation, making them effective against high-quality forgeries such as deepfakes. In contrast, image forensics provides a complementary layer of analysis through metadata inspection, noise inconsistencies, and compression artifact detection using methods like Error Level Analysis (ELA) and Photo-Response Non-Uniformity (PRNU). By integrating these two paradigms, the proposed framework aims to enhance detection accuracy, interpretability, and robustness, particularly in real-world scenarios. Experimental evaluations demonstrate that the hybrid model outperforms standalone methods in detecting various types of fake images.The research also tackles critical issues including resistance to adversarial attacks, the ability to generalize across diverse datasets, and adapting to the rapid advancements in synthetic image generation techniques.This research aims to enhance the reliability and interpretability of fake image detection tools, focusing on their readiness for use in sensitive and high-impact contexts.