  Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That
is, adversarial examples, obtained by adding delicately crafted distortions
onto original legal inputs, can mislead a DNN to classify them as any target
labels. In a successful adversarial attack, the targeted mis-classification
should be achieved with the minimal distortion added. In the literature, the
added distortions are usually measured by L0, L1, L2, and L infinity norms,
namely, L0, L1, L2, and L infinity attacks, respectively. However, there lacks
a versatile framework for all types of adversarial attacks.
  This work for the first time unifies the methods of generating adversarial
examples by leveraging ADMM (Alternating Direction Method of Multipliers), an
operator splitting optimization approach, such that L0, L1, L2, and L infinity
attacks can be effectively implemented by this general framework with little
modifications. Comparing with the state-of-the-art attacks in each category,
our ADMM-based attacks are so far the strongest, achieving both the 100% attack
success rate and the minimal distortion.
