scholarly journals Cross-Device Profiled Side-Channel Attack with Unsupervised Domain Adaptation

2021 ◽  
pp. 27-56
Author(s):  
Pei Cao ◽  
Chi Zhang ◽  
Xiangjun Lu ◽  
Dawu Gu
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Fine Tuning ◽  
Cross Entropy ◽  
Side Channel ◽  
Proof Of Concept ◽  
Maximum Mean Discrepancy ◽  
Unsupervised Domain Adaptation ◽  
Multiple Devices ◽  
The Impact

Deep learning (DL)-based techniques have recently proven to be very successful when applied to profiled side-channel attacks (SCA). In a real-world profiled SCA scenario, attackers gain knowledge about the target device by getting access to a similar device prior to the attack. However, most state-of-the-art literature performs only proof-of-concept attacks, where the traces intended for profiling and attacking are acquired consecutively on the same fully-controlled device. This paper reminds that even a small discrepancy between the profiling and attack traces (regarded as domain discrepancy) can cause a successful single-device attack to completely fail. To address the issue of domain discrepancy, we propose a Cross-Device Profiled Attack (CDPA), which introduces an additional fine-tuning phase after establishing a pretrained model. The fine-tuning phase is designed to adjust the pre-trained network, such that it can learn a hidden representation that is not only discriminative but also domain-invariant. In order to obtain domain-invariance, we adopt a maximum mean discrepancy (MMD) loss as a constraint term of the classic cross-entropy loss function. We show that the MMD loss can be easily calculated and embedded in a standard convolutional neural network. We evaluate our strategy on both publicly available datasets and multiple devices (eight Atmel XMEGA 8-bit microcontrollers and three SAKURA-G evaluation boards). The results demonstrate that CDPA can improve the performance of the classic DL-based SCA by orders of magnitude, which significantly eliminates the impact of domain discrepancy caused by different devices.

scholarly journals Threshold-Based Hierarchical Clustering for Person Re-Identification

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 522
Author(s):  
Minhui Hu ◽  
Kaiwei Zeng ◽  
Yaohua Wang ◽  
Yang Guo
Keyword(s):  
Hierarchical Clustering ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Fine Tuning ◽  
Single Sample ◽  
Clustering Methods ◽  
Clustering Method ◽  
Cluster Number ◽  
Unsupervised Domain Adaptation ◽  
And Training

Unsupervised domain adaptation is a challenging task in person re-identification (re-ID). Recently, cluster-based methods achieve good performance; clustering and training are two important phases in these methods. For clustering, one major issue of existing methods is that they do not fully exploit the information in outliers by either discarding outliers in clusters or simply merging outliers. For training, existing methods only use source features for pretraining and target features for fine-tuning and do not make full use of all valuable information in source datasets and target datasets. To solve these problems, we propose a Threshold-based Hierarchical clustering method with Contrastive loss (THC). There are two features of THC: (1) it regards outliers as single-sample clusters to participate in training. It well preserves the information in outliers without setting cluster number and combines advantages of existing clustering methods; (2) it uses contrastive loss to make full use of all valuable information, including source-class centroids, target-cluster centroids and single-sample clusters, thus achieving better performance. We conduct extensive experiments on Market-1501, DukeMTMC-reID and MSMT17. Results show our method achieves state of the art.

scholarly journals Does BERT need domain adaptation for clinical negation detection?

2020 ◽  
Vol 27 (4) ◽  
pp. 584-591 ◽  
Author(s):  
Chen Lin ◽  
Steven Bethard ◽  
Dmitriy Dligach ◽  
Farig Sadeque ◽  
Guergana Savova ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Adaptation Algorithm ◽  
Learning Methods ◽  
Clinical Text ◽  
Unsupervised Domain Adaptation ◽  
Adversarial Training ◽  
Negation Detection ◽  
Adaptation Method

Abstract Introduction Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue. Objective We examine neural unsupervised domain adaptation methods, introducing a novel combination of domain adaptation with transformer-based transfer learning methods to improve negation detection. We also want to better understand the interaction between the widely used bidirectional encoder representations from transformers (BERT) system and domain adaptation methods. Materials and Methods We use 4 clinical text datasets that are annotated with negation status. We evaluate a neural unsupervised domain adaptation algorithm and BERT, a transformer-based model that is pretrained on massive general text datasets. We develop an extension to BERT that uses domain adversarial training, a neural domain adaptation method that adds an objective to the negation task, that the classifier should not be able to distinguish between instances from 2 different domains. Results The domain adaptation methods we describe show positive results, but, on average, the best performance is obtained by plain BERT (without the extension). We provide evidence that the gains from BERT are likely not additive with the gains from domain adaptation. Discussion Our results suggest that, at least for the task of clinical negation detection, BERT subsumes domain adaptation, implying that BERT is already learning very general representations of negation phenomena such that fine-tuning even on a specific corpus does not lead to much overfitting. Conclusion Despite being trained on nonclinical text, the large training sets of models like BERT lead to large gains in performance for the clinical negation detection task.

scholarly journals Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 7618-7625
Author(s):  
Yong Dai ◽  
Jian Liu ◽  
Xiancong Ren ◽  
Zenglin Xu
Keyword(s):  
Sentiment Analysis ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Weak Assumption ◽  
Target Domain ◽  
Smoothness Assumption ◽  
Unsupervised Domain Adaptation ◽  
Good Target ◽  
Adversarial Training ◽  
Learning Frameworks

Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information. Existing algorithms of MS-UDA either only exploit the shared features, i.e., the domain-invariant information, or based on some weak assumption in NLP, e.g., smoothness assumption. To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypothesis. The key feature of the first framework is a novel Weighting Scheme based Unsupervised Domain Adaptation framework ((WS-UDA), which combine the source classifiers to acquire pseudo labels for target instances directly. While the second framework is a Two-Stage Training based Unsupervised Domain Adaptation framework (2ST-UDA), which further exploits these pseudo labels to train a target private extractor. Importantly, the weights assigned to each source classifier are based on the relations between target instances and source domains, which measured by a discriminator through the adversarial training. Furthermore, through the same discriminator, we also fulfill the separation of shared features and private features.Experimental results on two SA datasets demonstrate the promising performance of our frameworks, which outperforms unsupervised state-of-the-art competitors.

scholarly journals Triplet Loss Network for Unsupervised Domain Adaptation

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 96 ◽  
Author(s):  
Imad Eddine Ibrahim Bekkouch ◽  
Youssef Youssry ◽  
Rustam Gafarov ◽  
Adil Khan ◽  
Asad Masood Khattak
Keyword(s):  
Image Classification ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Traffic Sign ◽  
Linear Discriminant ◽  
Unsupervised Domain Adaptation ◽  
Sign Recognition ◽  
Almost All

Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to as TripNet) harnesses the idea of a discriminator and Linear Discriminant Analysis (LDA) to push the encoder to generate domain-invariant features that are category-informative. At the same time, pseudo-labelling is used for the target data to train the classifier and to bring the same classes from both domains together. We evaluate TripNet against several existing, state-of-the-art methods on three image classification tasks: Digit classification (MNIST, SVHN, and USPC datasets), object recognition (Office31 dataset), and traffic sign recognition (GTSRB and Synthetic Signs datasets). Our experimental results demonstrate that (i) TripNet beats almost all existing methods (having a similar simple model like it) on all of these tasks; and (ii) for models that are significantly more complex (or hard to train) than TripNet, it even beats their performance in some cases. Hence, the results confirm the effectiveness of using TripNet for unsupervised domain adaptation in image classification.

scholarly journals Domain Agnostic Real-Valued Specificity Prediction

2019 ◽  
Vol 33 ◽  
pp. 6610-6617 ◽  
Author(s):  
Wei-Jen Ko ◽  
Greg Durrett ◽  
Junyi Jessy Li
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Error Reduction ◽  
Level Of Detail ◽  
Reference Distribution ◽  
Unsupervised Domain Adaptation ◽  
Current State ◽  
Prediction Systems

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced realvalued specificity ratings.We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels. To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution. We show that our framework generalizes well to three different domains with 50%-68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity. We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.

scholarly journals Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation

2020 ◽  
Author(s):  
Pin Jiang ◽  
Aming Wu ◽  
Yahong Han ◽  
Yunfeng Shao ◽  
Meiyu Qi ◽  
...  
Keyword(s):  
Additional Data ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Powerful Method ◽  
Target Domain ◽  
Unsupervised Domain Adaptation ◽  
Benchmark Datasets ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Effective Use

Semi-supervised domain adaptation (SSDA) is a novel branch of machine learning that scarce labeled target examples are available, compared with unsupervised domain adaptation. To make effective use of these additional data so as to bridge the domain gap, one possible way is to generate adversarial examples, which are images with additional perturbations, between the two domains and fill the domain gap. Adversarial training has been proven to be a powerful method for this purpose. However, the traditional adversarial training adds noises in arbitrary directions, which is inefficient to migrate between domains, or generate directional noises from the source to target domain and reverse. In this work, we devise a general bidirectional adversarial training method and employ gradient to guide adversarial examples across the domain gap, i.e., the Adaptive Adversarial Training (AAT) for source to target domain and Entropy-penalized Virtual Adversarial Training (E-VAT) for target to source domain. Particularly, we devise a Bidirectional Adversarial Training (BiAT) network to perform diverse adversarial trainings jointly. We evaluate the effectiveness of BiAT on three benchmark datasets and experimental results demonstrate the proposed method achieves the state-of-the-art.

Weighted and Class-Specific Maximum Mean Discrepancy for Unsupervised Domain Adaptation

2020 ◽  
Vol 22 (9) ◽  
pp. 2420-2433
Author(s):  
Hongliang Yan ◽  
Zhetao Li ◽  
Qilong Wang ◽  
Peihua Li ◽  
Yong Xu ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Maximum Mean Discrepancy ◽  
Unsupervised Domain Adaptation

scholarly journals An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation

2020 ◽  
Vol 34 (07) ◽  
pp. 12613-12620 ◽  
Author(s):  
Jihan Yang ◽  
Ruijia Xu ◽  
Ruiyu Li ◽  
Xiaojuan Qi ◽  
Xiaoyong Shen ◽  
...  
Keyword(s):  
Marginal Distribution ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Feature Space ◽  
Semantic Segmentation ◽  
The State ◽  
Object Size ◽  
Feature Maps ◽  
Feature Representations ◽  
Unsupervised Domain Adaptation

We focus on Unsupervised Domain Adaptation (UDA) for the task of semantic segmentation. Recently, adversarial alignment has been widely adopted to match the marginal distribution of feature representations across two domains globally. However, this strategy fails in adapting the representations of the tail classes or small objects for semantic segmentation since the alignment objective is dominated by head categories or large objects. In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations. Specifically, we firstly perturb the intermediate feature maps with several attack objectives (i.e., discriminator and classifier) on each individual position for both domains, and then the classifier is trained to be invariant to the perturbations. By perturbing each position individually, our model treats each location evenly regardless of the category or object size and thus circumvents the aforementioned issue. Moreover, the domain gap in feature space is reduced by extrapolating source and target perturbed features towards each other with attack on the domain discriminator. Our approach achieves the state-of-the-art performance on two challenging domain adaptation tasks for semantic segmentation: GTA5 → Cityscapes and SYNTHIA → Cityscapes.

scholarly journals Stable Median Centre Clustering for Unsupervised Domain Adaptation Person Re-Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jifeng Guo ◽  
Wenbo Sun ◽  
Zhiqi Pang ◽  
Yuxiao Fei ◽  
Yu Chen
Keyword(s):  
Clustering Algorithm ◽  
Domain Adaptation ◽  
Target Domain ◽  
Improve Performance ◽  
Confidence Measure ◽  
Unsupervised Domain Adaptation ◽  
Unlabelled Data ◽  
The Impact ◽  
Neighbour Cluster ◽  
Adaptation Model

The current unsupervised domain adaptation person re-identification (re-ID) method aims to solve the domain shift problem and applies prior knowledge learned from labelled data in the source domain to unlabelled data in the target domain for person re-ID. At present, the unsupervised domain adaptation person re-ID method based on pseudolabels has obtained state-of-the-art performance. This method obtains pseudolabels via a clustering algorithm and uses these pseudolabels to optimize a CNN model. Although it achieves optimal performance, the model cannot be further optimized due to the existence of noisy labels in the clustering process. In this paper, we propose a stable median centre clustering (SMCC) for the unsupervised domain adaptation person re-ID method. SMCC adaptively mines credible samples for optimization purposes and reduces the impact of label noise and outliers on training to improve the performance of the resulting model. In particular, we use the intracluster distance confidence measure of the sample and its K-reciprocal nearest neighbour cluster proportion in the clustering process to select credible samples and assign different weights according to the intracluster sample distance confidence of samples to measure the distances between different clusters, thereby making the clustering results more robust. The experiments show that our SMCC method can select credible and stable samples for training and improve performance of the unsupervised domain adaptation model. Our code is available at https://github.com/sunburst792/SMCC-method/tree/master.

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