Mobile phone image-based framework for anti-copy pattern detection and classification

The proliferation of high-quality printing and reproduction technologies has exacerbated product counterfeiting. Manufacturers have employed anti-copy patterns to prevent unauthorised duplication, yet their effectiveness relies on the robustness of image processing systems. This paper presents, to the best of our knowledge, the first comprehensive mobile phone image-based framework for detecting and classifying anti-copy patterns in real-world industrial scenarios. Unlike prior studies that address segmentation, quality control, or feature extraction in isolation, our contribution lies in the non-trivial integration of these modules into a validated, end-to-end system. The framework combines the Segment Anything Model with an adaptive-angle cropping mechanism for precise segmentation, incorporates no-reference image quality assessment to filter unreliable inputs, and unifies spatial and frequency-domain features for robust representation. Dimensionality reduction and clustering then manage the feature pool efficiently. Validated on a real-world dataset of over 980 annotated samples, the system achieves 99.49% classification accuracy under varied imaging conditions, demonstrating both the feasibility and industrial applicability of an integrated pipeline for combating counterfeiting. • Developed a mobile phone image-based pipeline for anti-copy pattern (ACP) verification and classification. • Integrated Segment Anything Model (SAM) with adaptive-angle cropping for robust ACP segmentation. • Applied no-reference image quality assessment (NRIQA) for automated quality control. • Combined handcrafted spatial- and frequency-domain features to achieve discriminative representation. • Achieved 99.49% accuracy on a real-world industrial dataset of 985 mobile phone images.