RECTIFYING ADVERSARIAL EXAMPLES USING THEIR VULNERABILITIES

Rectifying Adversarial Examples Using Their Vulnerabilities

Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs).AEs are minimally perturbed input data undetectable to humans posing significant risks to security-dependent applications.Hence, extensive research has been undertaken to develop defense mechanisms that mitigate their threats.Most existing methods p

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Notes from the Field: The Humanitarian Crisis in Ukraine

Humanitarian crises are politically and socially charged, and as actors, donors and organizations move in to help, duplication of services can ensue.Despite the influx of humanitarian actors into the war zone of eastern Ukraine, more are still needed to address Power Amplifiers immediate threat to the health of more than 5 million at-risk people in

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Four Different Tumors Arising in a Nevus Sebaceous

Nevus sebaceous is known by its association with one or more secondary tumors, but more than three multiple tumors arising from a nevus sebaceous is extremely rare.A 67-year-old female presented ELP Conversion Kits with a light brown plaque on the back of her head that contained a dome-shaped black node and an erosive lesion.Histopathological exami

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