Adversarial Framing for Image and Video Classification

Understand

Neural networks are prone to adversarial attacks.

• In general, such attacks deteriorate the quality of the input by either slightly modifying most of its pixels, or by occluding it with a patch.
• In this paper, we propose a method that keeps the image unchanged and only adds an adversarial framing on the border of the image.
• We show empirically that our method is able to successfully attack state-of-the-art methods on both image and video classification problems.