Leveraging Adversarial Examples to Obtain Robust Second-Order Representations

25 Sep 2019  ·  Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib ·

Deep neural networks represent data as projections on trained weights in a high dimensional manifold. This is a first-order based absolute representation that is widely used due to its interpretable nature and simple mathematical functionality. However, in the application of visual recognition, first-order representations trained on pristine images have shown a vulnerability to distortions. Visual distortions including imaging acquisition errors and challenging environmental conditions like blur, exposure, snow and frost cause incorrect classification in first-order neural nets. To eliminate vulnerabilities under such distortions, we propose representing data points by their relative positioning in a high dimensional manifold instead of their absolute positions. Such a positioning scheme is based on a data point’s second-order property. We obtain a data point’s second-order representation by creating adversarial examples to all possible decision boundaries and tracking the movement of corresponding boundaries. We compare our representation against first-order methods and show that there is an increase of more than 14% under severe distortions for ResNet-18. We test the generalizability of the proposed representation on larger networks and on 19 complex and real-world distortions from CIFAR-10-C. Furthermore, we show how our proposed representation can be used as a plug-in approach on top of any network. We also provide methodologies to scale our proposed representation to larger datasets.

PDF Abstract
No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here