  It is now well known that deep neural networks (DNNs) are vulnerable to
adversarial attack. Adversarial samples are similar to the clean ones, but are
able to cheat the attacked DNN to produce incorrect predictions in high
confidence. But most of the existing adversarial attacks have high success rate
only when the information of the attacked DNN is well-known or could be
estimated by massive queries. A promising way is to generate adversarial
samples with high transferability. By this way, we generate 96020 transferable
adversarial samples from original ones in ImageNet. The average difference,
measured by root means squared deviation, is only around 3.8 on average.
However, the adversarial samples are misclassified by various models with an
error rate up to 90\%. Since the images are generated independently with the
attacked DNNs, this is essentially zero-query adversarial attack. We call the
dataset \emph{DAmageNet}, which is the first universal adversarial dataset that
beats many models trained in ImageNet. By finding the drawbacks, DAmageNet
could serve as a benchmark to study and improve robustness of DNNs. DAmageNet
could be downloaded in http://www.pami.sjtu.edu.cn/Show/56/122.
