  The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1),
making the DNNs-based systems collapsed byadding the inconspicuous
perturbations to the images. Most of the existing works for adversarial attack
are gradient-based and suf-fer from the latency efficiencies and the load on
GPU memory. Thegenerative-based adversarial attacks can get rid of this
limitation,and some relative works propose the approaches based on GAN.However,
suffering from the difficulty of the convergence of train-ing a GAN, the
adversarial examples have either bad attack abilityor bad visual quality. In
this work, we find that the discriminatorcould be not necessary for
generative-based adversarial attack, andpropose theSymmetric Saliency-based
Auto-Encoder (SSAE)to generate the perturbations, which is composed of the
saliencymap module and the angle-norm disentanglement of the featuresmodule.
The advantage of our proposed method lies in that it is notdepending on
discriminator, and uses the generative saliency map to pay more attention to
label-relevant regions. The extensive exper-iments among the various tasks,
datasets, and models demonstratethat the adversarial examples generated by SSAE
not only make thewidely-used models collapse, but also achieves good visual
quality.The code is available at https://github.com/BravoLu/SSAE.
