Tamper Detection in Multimodal Biometric Templates Using Fragile Watermarking and Artificial Intelligence

Biometric template protection is essential for finger-based authentication systems, as template tampering and adversarial attacks threaten the security. This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication. The system was tested against NIST SD4 and Anguli fingerprint datasets, wherein 10,000 watermarked fingerprints were employed for training. The designed approach recorded a tamper detection rate of 98.3%, performing 3–6% better than current DCT, SVD, and DWT-based watermarking approaches. The false positive rate (≤1.2%) and false negative rate (≤1.5%) were much lower compared to previous research, which maintained high reliability for template change detection. The system showed real-time performance, averaging 12–18 ms processing time per template, and is thus suitable for real-world biometric authentication scenarios. Quality analysis of fingerprints indicated that NFIQ scores were enhanced from 2.07 to 1.81, reflecting improved minutiae clarity and ridge structure preservation. The approach also exhibited strong resistance to compression and noise distortions, with the improvements in PSNR being 2 dB (JPEG compression Q = 80) and the SSIM values rising by 3%–5% under noise attacks. Comparative assessment demonstrated that training with NIST SD4 data greatly improved the ridge continuity and quality of fingerprints, resulting in better match scores (260–295) when tested against Bozorth3. Smaller batch sizes (batch = 2) also resulted in improved ridge clarity, whereas larger batch sizes (batch = 8) resulted in distortions. The DCNN-based tamper detection model supported real-time classification, which greatly minimized template exposure to adversarial attacks and synthetic fingerprint forgeries. Results demonstrate that fragile watermarking with AI indeed greatly enhances fingerprint security, providing privacy-preserving biometric authentication with high robustness, accuracy, and computational efficiency.