2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-5.9
Paper Title A HYPERSPECTRAL APPROACH FOR UNSUPERVISED SPOOF DETECTION WITH INTRA-SAMPLE DISTRIBUTION
Authors Tomoya Kaichi, Keio University, Japan; Yuko Ozasa, Tokyo Denki University, Japan
SessionMLR-APPL-IP-5: Machine learning for image processing 5
LocationArea E
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Despite the high recognition accuracy of recent deep neural networks, they can be easily deceived by spoofing. Spoofs (e.g., a printed photograph) visually resemble the actual objects quite closely. Thus, we propose a method for spoof detection with a hyperspectral image (HSI) that can effectively detect differences in surface materials. In contrast to existing anti-spoofing approaches, the proposed method learns the feature representation for spoof detection without spoof supervision. The informative pixels on an HSI are embedded onto the feature space, and we identify the spoof from their distribution. As this is the first attempt at unsupervised spoof detection with an HSI, a new dataset that includes spoofs, named Hyperspectral Spoof Dataset (HSSD), has been developed. The experimental results indicate that the proposed method performs significantly better than the baselines. The source code and the dataset are available on our project page.