Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems

Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown that finger-vein- and fingerprint-based authentication methods are susceptible to wolf attacks, in which a wolf sample matches many enrolled user templates. In this work, we demonstrated that wolf (generic) faces, which we call "master faces," can also compromise face recognition systems and that the master face concept can be generalized in some cases. Motivated by recent similar work in the fingerprint domain, we generated high-quality master faces by using the state-of-the-art face generator StyleGAN in a process called latent variable evolution. Experiments demonstrated that even attackers with limited resources using only pre-trained models available on the Internet can initiate master face attacks. The results, in addition to demonstrating performance from the attacker's point of view, can also be used to clarify and improve the performance of face recognition systems and harden face authentication systems.Comment: Accepted to be Published in Proceedings of the 2020 International Joint Conference on Biometrics (IJCB 2020), Houston, US

엣지 장비를 위한 한정된 데이터를 가지는 딥러닝 비전 어플리케이션의 빠른 적응

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022.2. 유승주.딥 러닝 기반 방법의 놀라운 성공은 주로 많은 양의 분류된 데이터로 달성되었다. 전통적인 기계 학습 방법과 비교해서 딥러닝 방법은 아주 큰 데이터셋으로부터 좋은 성능을 가진 모델을 학습할 수 있다. 하지만 고품질의 분류된 데이터는 만들기 어렵고 프라이버시 문제로 만들 수 없을 때도 있다. 게다가 사람은 아주 큰 분류된 데이터가 없어도 훌륭한 일반화 능력을 보여준다. 엣지 장비는 서버와 비교해서 제한적인 계산 능력을 가진다. 특히 학습 과정을 엣지 장비에서 수행하는 것은 매우 어렵다. 하지만, 도메인 변화 문제와 프라이버시 문제를 고려했을 때 엣지 장비에서 학습 과정을 수행하는 것은 바람직하다. 본 논문에서는 계산능력이 작은 엣지 장비를 위해 적응 과정을 전통적인 학습 과정 대신 고려한다. 전통적인 분류 문제는 학습 데이터와 테스트 데이터가 동일한 분포에서 파생되었음과 많은 양의 학습 데이터를 가정한다. 비지도 도메인 어댑테이션은 테스트 데이터가 학습데이터와 다른 분포에서 파생되는 상황을 가정하며 기존의 분류된 데이터와 학습된 모델을 이용해 새로운 데이터를 분류하는 문제이다. 퓨샷 학습은 적은 양의 학습 데이터를 가정하며 소수의 분류된 데이터만을 가지고 새로운 데이터를 분류하는 문제이다. 엣지 장비를 위해 이미지넷에서 미리 학습된 모델을 통해 비지도 도메인 어댑테이션 성능을 강화하는 방법과 지도 컨트라스티브 학습을 통해 퓨샷 학습 성능을 강화하는 방법을 제안하였다. 두 방법은 모두 적은 분류된 데이터 문제를 다루며 다만 서로 다른 시나리오를 가정한다. 첫 번째 방법은 엣지 장비를 위해 네트워크 모델과 파라미터 선택의 동시 최적화를 통해 비지도 도메인 어댑테이션 성능을 강화하는 방법이다. 이미지넷에서 미리 학습된 모델은 Office 데이터셋과 같이 작은 데이터셋을 다룰때 매우 중요하다. 특징 추출기를 갱신하지 않는 비지도 도메인 어댑테이션 알고리즘을 사용하고 아주 큰 이미지넷에서 미리 학습된 모델을 조합하는 방법으로 높은 정확도를 얻을 수 있다. 더 나아가 엣지 장비를 위해 작고 가벼운 이미지넷에서 미리 학습된 모델을 실험하였다. 지연시간을 줄이기 위해 예측기를 도입한 진화 알고리즘으로 방법의 시작부터 끝까지 최적화하였다. 그리고 프라이버시를 지키기 위한 비지도 도메인 어댑테이션 시나리오에 대해 고려하였다. 또한 엣지 장비에서 좀 더 현실적인 시나리오인 작은 데이터셋과 object detection 에 대해서도 실험하였다. 마지막으로 연속적인 데이터가 입력될 때 중간 데이터를 활용하여 지연시간을 더 감소시키는 방법을 실험하였다. Office31과 Office-Home 데이터셋에 대해 각각 5.99배와 9.06배 지연시간 감소를 달성하였다. 두 번째 방법은 지도 컨트라스티브 학습을 통해 퓨샷 학습 성능을 강화하는 방법이다. 퓨샷 학습 벤치마크에서는 베이스 데이터셋으로 특징 추출기를 학습하기 때문에 이미지넷에서 미리 학습된 모델을 사용할 수 없다. 대신에, 지도 컨트라스티브 학습을 통해 특징 추출기를 강화한다. 지도 컨트라스티브 학습과 정보 최대화 그리고 프로토타입 추정 방법을 조합하여 아주 높은 정확도를 얻을 수 있다. 특징 추출기와 미리 끝내기를 통해 이렇게 얻은 정확도를 수행시간 감소로 바꿀 수 있다. 트랜스덕티브 5-웨이 5-샷 학습 시나리오에서 3.87배 지연시간 감소를 달성하였다. 본 방법은 정확도를 증가시킨 후 지연시간을 감소시키는 방법으로 요약할 수 있다. 먼저 이미지넷에서 미리 학습된 모델을 쓰거나 지도 컨트라스티브 학습을 통해 특징 추출기를 강화해서 높은 정확도를 얻는다. 그 후 진화 알고리즘을 통해 시작부터 끝까지 최적화하거나 미리 끝내기를 통해 지연시간을 줄인다. 정확도를 증가시킨 후 지연시간을 감소시키는 두 단계 접근 방식은 엣지 장비를 위한 한정된 데이터를 가지는 딥러닝 비전 어플리케이션의 빠른 적응을 달성하는데 충분하다.The remarkable success of deep learning-based methods are mainly accomplished by a large amount of labeled data. Compared to conventional machine learning methods, deep learning-based methods are able to learn high quality model with a large dataset size. However, high-quality labeled data is expensive to obtain and sometimes preparing a large dataset is impossible due to privacy concern. Furthermore, human shows outstanding generalization performance without a huge amount of labeled data. Edge devices have a limited capability in computation compared to servers. Especially, it is challenging to implement training on edge devices. However, training on edge device is desirable when considering domain-shift problem and privacy concern. In this dissertation, I consider adaptation process as a conventional training counterpart for low computation capability edge device. Conventional classification assumes that training data and test data are drawn from the same distribution and training dataset is large. Unsupervised domain adaptation addresses the problem when training data and test data are drawn from different distribution and it is a problem to label target domain data using already existing labeled data and models. Few-shot learning assumes small training dataset and it is a task to predict new data based on only a few labeled data. I present 1) co-optimization of backbone network and parameter selection in unsupervised domain adaptation for edge device and 2) augmenting few-shot learning with supervised contrastive learning. Both methods are targeting low labeled data regime but different scenarios. The first method is to boost unsupervised domain adaptation by co-optimization of backbone network and parameter selection for edge device. Pre-trained ImageNet models are crucial when dealing with small dataset such as Office datasets. By using unsupervised domain adaptation algorithm that does not update feature extractor, large and powerful pre-trained ImageNet models can be used to boost the accuracy. We report state-of-the-art accuracy result with the method. Moreover, we conduct an experiment to use small and lightweight pre-trained ImageNet models for edge device. Co-optimization is performed to reduce the total latency by using predictor-guided evolutionary search. We also consider pre-extraction of source feature. We conduct more realistic scenario for edge device such as smaller target domain data and object detection. Lastly, We conduct an experiment to utilize intermediate domain data to reduce the algorithm latency further. We achieve 5.99x and 9.06x latency reduction on Office31 and Office-Home dataset, respectively. The second method is to augment few-shot learning with supervised contrastive learning. We cannot use pre-trained ImageNet model in the few-shot learning benchmark scenario as they provide base dataset to train the feature extractor from scratch. Instead, we augment the feature extractor with supervised contrastive learning method. Combining supervised contrastive learning with information maximization and prototype estimation technique, we report state-of-the-art accuracy result with the method. Then, we translate the accuracy gain to total runtime reduction by changing the feature extractor and early stopping. We achieve 3.87x latency reduction for transductive 5-way 5-shot learning scenarios. Our approach can be summarized as boosting the accuracy followed by latency reduction. We first upgrade the feature extractor by using more advanced pre-trained ImageNet model or by supervised contrastive learning to achieve state-of-the-art accuracy. Then, we optimize the method end-to-end with evolutionary search or early stopping to reduce the latency. Our two stage approach which consists of accuracy boosting and latency reduction is sufficient to achieve fast adaptation of deep learning vision applications with limited data for edge device.1. Introduction 1 2. Background 7 2.1 Dataset Size for Vision Applications 7 2.2 ImageNet Pre-trained Models 9 2.3 Augmentation Methods for ImageNet 12 2.4 Contrastive Learning 14 3. Problem Definitions and Solutions Overview 17 3.1 Problem Definitions 17 3.1.1 Unsupervised Domain Adaptation 17 3.1.2 Few-shot learning 18 3.2 Solutions overview 19 3.2.1 Co-optimization of Backbone Network and Parameter Selection in Unsupervised Domain Adaptation for Edge Device 20 3.2.2 Augmenting Few-Shot Learning with Supervised Contrastive Learning 21 4. Co-optimization of Backbone Network and Parameter Selection in Unsupervised Domain Adaptation for Edge Device 22 4.1 Introduction 23 4.2 Related Works 28 4.3 Methodology 33 4.3.1 Examining an Unsupervised Domain Adaptation Method 33 4.3.2 Boosting Accuracy with Pre-Trained ImageNet Models 36 4.3.3 Boosting Accuracy for Edge Device 38 4.3.4 Co-optimization of Backbone Network and Parameter Selection 39 4.4 Experiments 41 4.4.1 ImageNet and Unsupervised Domain Adaptation Accuracy 43 4.4.2 Accuracy with Once-For-All Network 52 4.4.3 Comparison with State-of-the-Art Results 58 4.4.4 Co-optimization for Edge Device 59 4.4.5 Pre-extraction of Source Feature 72 4.4.6 Results for Small Target Data Scenario 77 4.4.7 Results for Object Detection 78 4.4.8 Results for Classifier Fitting Using Intermediate Domain 80 4.4.9 Summary 81 4.5 Conclusion 84 5. Augmenting Few-Shot Learning with Supervised Contrastive Learning 85 5.1 Introduction 86 5.2 Related Works 89 5.3 Methodology 92 5.3.1 Examining A Few-shot Learning Method 92 5.3.2 Augmenting Few-shot Learning with Supervised Contrastive Learning 94 5.4 Experiments 97 5.4.1 Comparison to the State-of-the-Art 99 5.4.2 Ablation Study 102 5.4.3 Domain-Shift 105 5.4.4 Increasing the Number of Ways 106 5.4.5 Runtime Analysis 107 5.4.6 Limitations 109 5.5 Conclusion 110 6. Conclusion 111박

Confidence-and-Refinement Adaptation Model for Cross-Domain Semantic Segmentation

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Deep learning for time series classification

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Advances in medical imaging have greatly aided in providing accurate and fast medical diagnosis, followed by recent deep learning developments enabling the efficient and cost-effective analysis of medical images. Among different image processing tasks, medical segmentation is one of the most crucial aspects because it provides the class, location, size, and shape of the subject of interest, which is invaluable and essential for diagnostics. Nevertheless, acquiring annotations for training data usually requires expensive manpower and specialised expertise, making supervised training difficult. To overcome these problems, unsupervised domain adaptation (UDA) has been adopted to bridge knowledge between different domains. Despite the appearance dissimilarities of different modalities such as MRI and CT, researchers have concluded that structural features of the same anatomy are universal across modalities, which unfolded the study of multi-modality image segmentation with UDA methods. The traditional UDA research tackled the domain shift problem by minimising the distance of the source and target distributions in latent spaces with the help of advanced mathematics. However, with the recent development of the generative adversarial network (GAN), the adversarial UDA methods have shown outstanding performance by producing synthetic images to mitigate the domain gap in training a segmentation network for the target domain. Most existing studies focus on modifying the network architecture, but few investigate the generative adversarial training strategy. Inspired by the recent success of state-of-the-art data augmentation techniques in classification tasks, we designed a novel mix-up strategy to assist GAN training for the better synthesis of structural details, consequently leading to better segmentation results. In this thesis, we propose SynthMix, an add-on module with a natural yet effective training policy that can promote synthetic quality without altering the network architecture. SynthMix is a mix-up synthesis scheme designed for integration with the adversarial logic of GAN networks. Traditional GAN approaches judge an image as a whole which could be easily dominated by discriminative features, resulting in little improvement of delicate structures in synthesis. In contrast, SynthMix uses the data augmentation technique to reinforce detail transformation at local regions. Specifically, it coherently mixes up aligned images of real and synthetic samples at local regions to stimulate the generation of fine-grained features examined by an associated inspector for domain-specific details. We evaluated our method on two segmentation benchmarks among three publicly available datasets. Our method showed a significant performance gain compared with existing state-of-the-art approaches