의미보존 적대적 학습

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 이상구.Adversarial training is a defense technique that improves adversarial robustness of a deep neural network (DNN) by including adversarial examples in the training data. In this paper, we identify an overlooked problem of adversarial training in that these adversarial examples often have different semantics than the original data, introducing unintended biases into the model. We hypothesize that such non-semantics-preserving (and resultingly ambiguous) adversarial data harm the robustness of the target models. To mitigate such unintended semantic changes of adversarial examples, we propose semantics-preserving adversarial training (SPAT) which encourages perturbation on the pixels that are shared among all classes when generating adversarial examples in the training stage. Experiment results show that SPAT improves adversarial robustness and achieves state-of-the-art results in CIFAR-10, CIFAR-100, and STL-10.적대적 학습은 적대적 예제를 학습 데이터에 포함시킴으로써 심층 신경망의 적대적 강건성을 개선하는 방어 방법이다. 이 논문에서는 적대적 예제들이 원본 데이터와는 때때로 다른 의미를 가지며, 모델에 의도하지 않은 편향을 집어 넣는다는 기존에는 간과되어왔던 적대적 학습의 문제를 밝힌다. 우리는 이러한 의미를 보존하지 않는, 그리고 결과적으로 애매모호한 적대적 데이터가 목표 모델의 강건성을 해친다고 가설을 세웠다. 우리는 이러한 적대적 예제들의 의도하지 않은 의미적 변화를 완화하기 위해, 학습 단계에서 적대적 예제들을 생성할 때 모든 클래스들에게서 공유되는 픽셀에 교란하도록 권장하는, 의미 보존 적대적 학습을 제안한다. 실험 결과는 의미 보존 적대적 학습이 적대적 강건성을 개선하며, CIFAR-10과 CIFAR-100과 STL-10에서 최고의 성능을 달성함을 보인다.Chapter 1 Introduction 1 Chapter 2 Preliminaries 5 Chapter 3 Related Works 9 Chapter 4 Semantics-Preserving Adversarial Training 11 4.1 Problem of PGD-training . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Semantics-Preserving Adversarial Training . . . . . . . . . . . . . 13 4.3 Combining with Adversarial Training Variants . . . . . . . . . . 14 Chapter 5 Analysis of Adversarial Examples 16 5.1 Visualizing Various Adversarial Examples . . . . . . . . . . . . . 16 5.2 Comparing the Attack Success Rate . . . . . . . . . . . . . . . . 17 Chapter 6 Experiments & Results 22 6.1 Evaluating Robustness . . . . . . . . . . . . . . . . . . . . . . . . 22 6.1.1 CIFAR-10 & CIFAR-100 . . . . . . . . . . . . . . . . . . . 22 6.1.2 CIFAR-10 with 500K Unlabeled Data . . . . . . . . . . . 24 6.1.3 STL-10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 6.2 Effect of Label Smoothing Hyperparameterα. . . . . . . . . . . 25 Chapter 7 Conclusion & Future Work 29Maste

Domain-adaptive Message Passing Graph Neural Network

Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To address CNNC, we propose a domain-adaptive message passing graph neural network (DM-GNN), which integrates graph neural network (GNN) with conditional adversarial domain adaptation. DM-GNN is capable of learning informative representations for node classification that are also transferrable across networks. Firstly, a GNN encoder is constructed by dual feature extractors to separate ego-embedding learning from neighbor-embedding learning so as to jointly capture commonality and discrimination between connected nodes. Secondly, a label propagation node classifier is proposed to refine each node's label prediction by combining its own prediction and its neighbors' prediction. In addition, a label-aware propagation scheme is devised for the labeled source network to promote intra-class propagation while avoiding inter-class propagation, thus yielding label-discriminative source embeddings. Thirdly, conditional adversarial domain adaptation is performed to take the neighborhood-refined class-label information into account during adversarial domain adaptation, so that the class-conditional distributions across networks can be better matched. Comparisons with eleven state-of-the-art methods demonstrate the effectiveness of the proposed DM-GNN