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Nowadays, digital facial content manipulation has become ubiquitous andrealistic with the success of generative adversarial networks (GANs), makingface recognition (FR) systems suffer from unprecedented security concerns. Inthis paper, we investigate and introduce a new type of adversarial attack toevade FR systems by manipulating facial content, called\textbf{\underline{a}dversarial \underline{mor}phing \underline{a}ttack}(a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixelintensity values by adding human-imperceptible noise, our proposed adversarialmorphing attack works at the semantic level that perturbs pixels spatially in acoherent manner. To tackle the black-box attack problem, we devise a simple yeteffective joint dictionary learning pipeline to obtain a proprietary opticalflow field for each attack. Our extensive evaluation on two popular FR systemsdemonstrates the effectiveness of our adversarial morphing attack at variouslevels of morphing intensity with smiling facial expression manipulations. Bothopen-set and closed-set experimental results indicate that a novel black-boxadversarial attack based on local deformation is possible, and is vastlydifferent from additive noise attacks. The findings of this work potentiallypave a new research direction towards a more thorough understanding andinvestigation of image-based adversarial attacks and defenses.

Amora: Black-box Adversarial Morphing Attack