Abstract: Adversarial in recent works target at closed recognition systems, in which the and testing classes are identical. In real-world scenarios, however, the testing classes may have limited, if any, overlap with the classes, a problem named set recognition. To our knowledge, the community does not have a specific design of adversarial examples targeting at this practical setting. Arguably, the new setting compromises traditional closed set attack methods in two aspects. First, closed set attack methods are based on classification and target at classification as well, but the set problem suggests a different task, emph{i.e.,} retrieval. It is undesirable that the generation mechanism of closed set recognition is different from the aim of set recognition. Second, given that the query image is usually of an unseen class, predicting its category from the classes is not reasonable, which leads to an inferior adversarial gradient. In this work, we view open set recognition as a retrieval task and propose a new approach, Opposite-Direction Feature Attack (ODFA), to generate adversarial examples / queries. When using an attacked example as query, we aim that the true matches be ranked as low as possible. In addressing the two limitations of closed set attack methods, ODFA directly works on the features for retrieval. The idea is to push away the feature of the adversarial query in the opposite direction of the original feature. Albeit simple, ODFA leads to a larger drop in Recall@K and mAP than the close- set attack methods on two open set recognition datasets, emph{i.e.,} Market-1501 and CUB-200-2011. We also demonstrate that the attack performance of ODFA is not evidently superior to the state-of-the-art methods under closed set recognition (Cifar-), suggesting its specificity for open set problems.



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