Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Empirical results in Section 3.1 in the main paper show that simply removing ReLUs lead to improved transferability. In this section, we try freezing all learnable parameters in the unmodified sub-net h during fine-tuning and a similar observation about the initial improvement of transferability can still be decrease made and (see finally Figure the 5). Classification loss of these modified VGG-19 models on the benign CIFAR-10 test set is also reported, in Figure 6. On ImageNet, it is evaluated on the 50000official validation images. As mentioned in the main paper, many recent successes in improving adversarial transferability benefit from maximizing intermediate level distortions rather than the final prediction losses [8, 3, 2] of DNNs.

The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community. In this paper, we study the transferability of such examples, which lays the foundation of many black-box attacks on DNNs. We revisit a not so new but definitely noteworthy hypothesis of Goodfellow et al.'s and disclose that the transferability can be enhanced by improving the linearity of DNNs in an appropriate manner. We introduce linear backpropagation (LinBP), a method that performs backpropagation in a more linear fashion using off-the-shelf attacks that exploit gradients. More specifically, it calculates forward as normal but backpropagates loss as if some nonlinear activations are not encountered in the forward pass. Experimental results demonstrate that this simple yet effective method obviously outperforms current state-of-the-arts in crafting transferable adversarial examples on CIFAR-10 and ImageNet, leading to more effective attacks on a variety of DNNs.

Neural Information Processing Systems http://nips.cc/

A new scale of humiliation ritual kicked off this week as Elon Musk's lawsuit against OpenAI went to trial in Silicon Valley. The Tesla CEO, who co-founded OpenAI, is suing the artificial intelligence firm and two of its other co-founders, Sam Altman and Greg Brockman, for diverting from its original nonprofit goal of developing A.I. for the public good in favor of for-profit motives. "This lawsuit is very simple: It is not OK to steal a charity," Musk said on the witness stand on Tuesday. The trial is big by every conceivable measure. Both Musk and OpenAI have mustered high-dollar legal armies who are prepared to wage potentially years of litigation, including this federal trial.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/