Deep learning has continued to gain momentum in applications across many critical areas of research in computer vision and machine learning. In particular, deep learning networks have had much success in image classification, especially when training data are abundantly available, as is the case with the ImageNet project. However, several researchers have exposed potential vulnerabilities of these networks to carefully crafted adversarial imagery. Additionally, researchers have shown the sensitivity of these networks to some types of noise and distortion. In this paper, we investigate the use of no-reference image quality metrics to identify adversarial imagery and images of poor quality that could potentially fool a deep learning network or dramatically reduce its accuracy. Results are shown on several adversarial image databases with comparisons to popular image classification databases.
Josh Harguess, Jeremy Miclat, and Julian Raheema, "Using image quality metrics to identify adversarial imagery for deep learning networks," Proc. SPIE 10199, Geospatial Informatics, Fusion, and Motion Video Analytics VII, 1019907 (Presented at SPIE Defense + Security: April 12, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2263584.
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