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The team therefore started to create composite images and test them with the YOLOv3 object detection system to see if they could effectively hide things by placing them next to objects that computer vision systems have been trained to see as unlikely correlations.
That approach saw computer vision systems suggest that mashup images of dogs and cats depicted a horse.
Another interesting result came with images of STOP signs and fruit. Computer vision systems spotted the fruit, but could not identify the STOP sign. The researchers created a STOP sign, photographed it against odd backgrounds, and succeeded in making computer vision systems fail to detect it. ....

Paul Ziegler , Minn Pa , Masaki Kamizono , Deloitte Tohmatsu Cyber , Black Hat Asia Computer , Black Hat Asia , Deloitte Japan , Common Objects In Context , பால் ஸீக்லர் , கருப்பு தொப்பி ஆசியா , டெலோய்ட்டே ஜப்பான் ,