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Deep neural network based object detection has become the cornerstone of many real-world applications. Along with this success comes concerns about its vulnerability to malicious attacks. To gain more insight into this issue, we propose a contextual camouflage attack (CCA for short) algorithm to influence the performance of object detectors. In this paper, we use an evolutionary search strategy and adversarial machine learning in interactions with a photo-realistic simulated environment to find camouflage patterns that are effective over a huge variety of object locations, camera poses, and lighting conditions. The proposed camouflages are validated effective to most of the stateof-the-art object detectors.

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The aim of this research is to present a novel computer-aided decision support tool in analyzing, quantifying, and evaluating the retinal blood vessel structure from fluorescein angiogram (FA) videos. Methods: The proposed method consists of three phases: (i) image registration for large motion removal from fluorescein angiogram videos, followed by (ii) retinal vessel segmentation, and lastly, (iii) segmentation-guided video magnification. In the image registration phase, individual frames of the video are spatiotemporally aligned using a novel wavelet-based registration approach to compensate for the global camera and patient motion. In the second phase, a capsule-based neural network architecture is employed to perform the segmentation of retinal vessels for the first time in the literature. In the final phase, a segmentation-guided Eulerian video magnification is proposed for magnifying subtle changes in the retinal video produced by blood flow through the retinal vessels. The magnification is applied only to the segmented vessels, as determined by the capsule network. This minimizes the high levels of noise present in these videos and maximizes useful information, enabling ophthalmologists to more easily identify potential regions of pathology. Results: The collected fluorescein angiogram video dataset consists of 1, 402 frames from 10 normal subjects (prospective study). Experimental results for retinal vessel segmentation show that the capsule-based algorithm outperforms a state-of-the-art convolutional neural networks (U-Net), obtaining a higher dice coefficient (85.94%) and sensitivity (92.36%) while using just 5% of the network parameters. Qualitative analysis of these videos was performed after the final phase by expert ophthalmologists, supporting the claim that artificial intelligence assisted decision support tool can be helpful for providing a better analysis of blood flow dynamics. Conclusions: The authors introduce a novel computational tool, combining a wavelet-based video registration method with a deep learning capsule-based retinal vessel segmentation algorithm and a Eulerian video magnification technique to quantitatively and qualitatively analyze FA videos. To authors' best knowledge, this is the first-ever development of such a computational tool to assist ophthalmologists with analyzing blood flow in FA videos.

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