An Efficient ROI-Based Transfer Learning to Discriminate Spoof Attacks
Keywords:
Advanced computer-aided systems, antispoofing, dual unit framework, YOLOV5s, transfer learning, IDIAP-Replay Attack, data augmentationAbstract
Modern video processing tools and advanced computer-aided systems have made it difficult to distinguish between real and fake identities. The spoofing tools nowadays are very powerful to fool the best antispoofing framework. The article presents a simple but robust dual-unit framework for preprocessing and blind feature extraction plus classification to discriminate between real and fake subjects. A low-complexity preprocessing unit to enhance the image details and a quality feature extraction module using YOLOV5s are introduced using transfer learning. The dual unit framework is evaluated over IDIAP-Replay attack dataset images obtained 98.39% classification accuracy for distinguishing the authentic and the fooled samples. The work does not use data augmentation, or face alignments, and performs well on imbalance classes having uneven foreground illumination samples.
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