A Review of Machine Learning Classifiers for Feature-Based Image Forgery Detection
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Abstract
The increasing use of digital image manipulation and the generation of synthetic images has led to serious concerns regarding the authenticity and trustworthiness of images. The recent development of advanced generative models like StyleGAN, StyleGAN2, and diffusion models has the potential to generate very realistic synthetic images that are very difficult to distinguish from real images, thus leading to serious challenges in digital image forensics. This paper presents a comprehensive review of image forgery detection techniques with a primary focus on machine learning-based techniques. Various image forgery techniques, including traditional image forgery and AI-created synthetic images, are presented to understand their nature and challenges in image forgery detection. The paper presents a review of machine learning-based methods, popular datasets, and evaluation metrics for image forgery detection. Furthermore, the paper presents the challenges of existing methods, particularly their poor generalization capability for diffusion-based forgeries and the lack of representative benchmarks, and concludes with open challenges and future research avenues for developing generalized machine learning methods for authentic detection of image forgeries.
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