Abstract
This work introduces FLIDS (Fuzzy Logic-based Image Distortion Scoring), an interpretable and efficient system for image tampering detection based on hand-crafted features and fuzzy logic. FLIDS combines JPEG artifact analysis, edge consistency, co-occurrence entropy, and CFA disparities into a fuzzy rule-based system for assigning a tampering confidence score. In contrast to black-box deep learning systems, FLIDS prioritizes transparency and generalizability. Tests on CIFAR-10, MNIST, ImageNet Subset, and Deepfake datasets indicate FLIDS attains competitive accuracy compared to ResNet-18, Autoencoder, and hand-designed JPEG detectors in the majority of instances. FLIDS achieves 93.5% and 91.8% accuracy on CIFAR-10 and ImageNet Subset, respectively, as well as a balanced 90.2% on deepfake datasets. These findings point to FLIDS as a promising, interpretable solution to intricate deep learning systems in image forgery detection.
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