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Leveraging Adversarial Examples to Quantify Membership Information Leakage

Leveraging Adversarial Examples to Quantify Membership Infor...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Del Grosso, Ganesh Jalalzai, Hamid Pichler, Georg Palamidessi, Catuscia Piantanida, Pablo Ecole Polytech Lab Informat INRIA Palaiseau France TU Wien Vienna Austria Univ Paris Saclay Cent Supelec CNRS MILAETSMcGillInt Lab Learning Syst ILLS Gif Sur Yvette France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today. Identifying training data based on a trained model is a standard way of measuring the privacy risks induced by the model. We develop a novel approach to address the problem of membership inference in pattern recognition models, relying on information provided by adversarial examples. The strategy we propose consists of measuring the magnitude of a perturbation necessary to build an adversarial example. Indeed, we argue that this quantity reflects the likelihood of belonging to the training data. Extensive numerical experiments on multivariate data and an array of state-of-the-art target models show that our method performs comparable or even outperforms stateof-the-art strategies, but without requiring any additional training samples.

关键词： Training Data privacy computer vision Perturbation methods Training data Machine learning Data models

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Appearance-based Gaze Estimation using Attention and Difference Mechanism

Appearance-based Gaze Estimation using Attention and Differe...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Murthy, L. R. D. Biswas, Pradipta Indian Inst Sci CPDM I3D Lab Bangalore Karnataka India

ISBN: (纸本)9781665448994

Appearance-based gaze estimation problem received wide attention over the past few years. Even though model-based approaches existed earlier, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. In this paper, we proposed two novel techniques to improve gaze estimation accuracy. Our first approach, I2D-Net uses a difference layer to eliminate any common features from left and right eyes of a participant that are not pertinent to gaze estimation task. Our second approach, AGE-Net adapted the idea of attention-mechanism and assigns weights to the features extracted from eye images. I2D-Net performed on par with the existing state-of-the-art approaches while AGE-Net reported state-of-the-art accuracy of 4.09 degrees and 7.44 degrees error on MPIIGaze and RT-Gene datasets respectively. We performed ablation studies to understand the effectiveness of the proposed approaches followed by analysis of gaze error distribution with respect to various factors of MPIIGaze dataset.

关键词： Deep learning computer vision Adaptation models conferences Estimation Lighting Feature extraction

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FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance

FaceQvec: Vector Quality Assessment for Face Biometrics base...

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22nd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hernandez-Ortega, Javier Fierrez, Julian Gomez, Luis F. Morales, Aythami Gonzalez-de-Suso, Jose Luis Zamora-Martinez, Francisco Univ Autonoma Madrid Sch Engn Madrid Spain Veridas Digital Authenticat Solut Pamplona Spain

ISBN: (纸本)9781665458245

In this paper we develop FaceQvec, a software component for estimating the conformity of facial images with each of the points contemplated in the ISO/IEC 19794-5, a quality standard that defines general quality guidelines for face images that would make them acceptable or unacceptable for use in official documents such as passports or ID cards. This type of tool for quality assessment can help to improve the accuracy of face recognition, as well as to identify which factors are affecting the quality of a given face image and to take actions to eliminate or reduce those factors, e.g., with postprocessing techniques or re-acquisition of the image. FaceQvec consists of the automation of 25 individual tests related to different points contemplated in the aforementioned standard, as well as other characteristics of the images that have been considered to be related to facial quality. We first include the results of the quality tests evaluated on a development dataset captured under realistic conditions. We used those results to adjust the decision threshold of each test. Then we checked again their accuracy on a evaluation database that contains new face images not seen during development. The evaluation results demonstrate the accuracy of the individual tests for checking compliance with ISO/IEC 19794-5. FaceQvec is available online(1).

关键词： computer vision Databases Face recognition ISO Standards conferences Software Quality assessment

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Real-time and Robust System for Counting Movement-Specific Vehicle at Crowded Intersections

Real-time and Robust System for Counting Movement-Specific V...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vu-Hoang Tran Le-Hoai-Hieu Dang Chinh-Nghiep Nguyen Ngoc-Hoang-Lam Le Khanh-Phong Bui Lam-Truong Dam Quang-Thang Le Dinh-Hiep Huynh Ho Chi Minh City Univ Technol & Educ Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

In order to reduce traffic congestion and improve the efficiency of traffic light signals, intelligent traffic systems are being developed by researchers, and vehicle counting is one of the key techniques in the system. The traditional methods mostly focus on increasing the vehicle counting effectiveness without regard to the program execution efficiency. The practical value of these systems will be reduced if they cannot be operated in real-time on compact IoT device. Therefore, in this paper, we mainly focus on designing a real-time and robust system for the problem of counting specific-movement vehicles. The system is able to detect and track objects in the area of interest, then count those tracked trajectories using the movements. To improve performance of tracking multiple objects, a high recall detection method and an efficient feature matching strategy were proposed. Moreover, to minimize the wrong direction of movement prediction and improve the results of vehicle counting, a cosine similarity-based vehicle counting scheme is applied. Experiments are conducted on AI City 2021 Track-1 dataset. Our method is evaluated on both sides of efficiency and effectiveness.

关键词： Performance evaluation computer vision Tracking conferences Urban areas Feature extraction Real-time systems

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The Casual Conversations v2 Dataset : A diverse, large benchmark for measuring fairness and robustness in audio/vision/speech models

The Casual Conversations v2 Dataset : A diverse, large bench...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Bilal Porgali Vítor Albiero Jordan Ryda Cristian Canton Ferrer Caner Hazirbas Meta AI

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators. The dataset includes 26,467 videos of 5,567 unique paid participants, with an average of almost 5 videos per person, recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the USA, representing diverse demographic characteristics. The participants agreed for their data to be used in assessing fairness of AI models and provided self-reported age, gender, language/dialect, disability status, physical adornments, physical attributes and geo-location information, while trained annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk Skin Tone scales, and voice timbre. Annotators also labeled for different recording setups and per-second activity annotations.

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Spectral Unsupervised Domain Adaptation for Visual recognition

Spectral Unsupervised Domain Adaptation for Visual Recogniti...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Jingyi Huang, Jiaxing Tian, Zichen Lu, Shijian Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Though unsupervised domain adaptation (UDA) has achieved very impressive progress recently, it remains a great challenge due to missing target annotations and the rich discrepancy between source and target distributions. We propose Spectral UDA (SUDA), an effective and efficient UDA technique that works in the spectral space and can generalize across different visual recognition tasks. SUDA addresses the UDA challenges from two perspectives. First, it introduces a spectrum transformer (ST) that mitigates inter-domain discrepancies by enhancing domain-invariant spectra while suppressing domain-variant spectra of source and target samples simultaneously. Second, it introduces multi-view spectral learning that learns useful unsupervised representations by maximizing mutual information among multiple ST-generated spectral views of each target sample. Extensive experiments show that SUDA achieves superior accuracy consistently across different visual tasks in object detection, semantic segmentation and image classification. Additionally, SUDA also works with the transformer-based network and achieves state-of-the-art performance on object detection.

关键词： Visualization computer vision Image segmentation Semantics Object detection Transformers pattern recognition

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Wide Receptive Field and Channel Attention Network for JPEG Compressed Image Deblurring

Wide Receptive Field and Channel Attention Network for JPEG ...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Lee, Donghyeon Lee, Chulhee Kim, Taesung Samsung Elect Hwaseong South Korea Sunmoon Univ Asan South Korea

ISBN: (纸本)9781665448994

A motion blurred image stored in the joint photographic experts group (JPEG) image compression format contains both motion blur and JPEG artifacts. Therefore, it is very difficult to restore the original image from a blurred and JPEG-compressed image. To address this problem, this paper proposes two methods: a wide receptive field and channel attention network (WRCAN), and JPEG autoencoder loss. First, the WRCAN utilizes a large receptive field and considers the interdependencies among channels of a feature map. Second, the proposed JPEG auto-encoder loss enables the WRCAN to learn prior knowledge of JPFG compression artifacts such that the proposed WRCAN can effectively restore the original image from JPEG-compressed images. The proposed methods are evaluated on the JPEG-compressed REDS dataset by participating in the NTIRE 2021 workshop challenges on Image Deblurring Track 2 JPEG artifacts. The WRCAN trained with the proposed loss ranked third with an output of 29.60dB on the REDS test set, indicating that the proposed methods provide state-of-the-art results. The source codes, model, and data are available at https://***/dhyeonlee/WRCAN-PyTorch.

关键词： computer vision Image coding Convolution conferences Transform coding computer architecture Data models

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Effective conditioned and composed image retrieval combining CLIP-based features

Effective conditioned and composed image retrieval combining...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Baldrati, Alberto Bertini, Marco Uricchio, Tiberio Del Bimbo, Alberto Univ Firenze MICC Florence Italy Univ Pisa Pisa Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Conditioned and composed image retrieval extend CBIR systems by combining a query image with an additional text that expresses the intent of the user, describing additional requests w.r.t. the visual content of the query image. This type of search is interesting for e-commerce applications, e.g. to develop interactive multimodal searches and chatbots. In this demo, we present an interactive system based on a combiner network, trained using contrastive learning, that combines visual and textual features obtained from the OpenAI CLIP network to address conditioned CBIR. The system can be used to improve e-shop search engines. For example, considering the fashion domain it lets users search for dresses, shirts and toptees using a candidate start image and expressing some visual differences w.r.t. its visual content, e.g. asking to change color, pattern or shape. The proposed network obtains state-of-the-art performance on the FashionIQ dataset and on the more recent CIRR dataset, showing its applicability to the fashion domain for conditioned retrieval, and to more generic content considering the more general task of composed image retrieval.

关键词： Visualization Shape Image retrieval Pipelines Search engines pattern recognition Electronic commerce

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Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

Supervised Contrastive Replay: Revisiting the Nearest Class ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Mai, Zheda Li, Ruiwen Kim, Hyunwoo Sanner, Scott Univ Toronto Toronto ON Canada LG AI Res Seoul South Korea

ISBN: (纸本)9781665448994

Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting. While memory replay has shown promising results, the recency bias in online learning caused by the commonly used Softmax classifier remains an unsolved challenge. Although the Nearest-Class-Mean (NCM) classifier is significantly undervalued in the CL community, we demonstrate that it is a simple yet effective substitute for the Softmax classifier. It addresses the recency bias and avoids structural changes in the fully-connected layer for new classes. Moreover, we observe considerable and consistent performance gains when replacing the Softmax classifier with the NCM classifier for several state-of-the-art replay methods. To leverage the NCM classifier more effectively, data embeddings belonging to the same class should be clustered and well-separated from those with a different class label. To this end, we contribute Supervised Contrastive Replay (SCR), which explicitly encourages samples from the same class to cluster tightly in embedding space while pushing those of different classes further apart during replay-based training. Overall, we observe that our proposed SCR substantially reduces catastrophic forgetting and outperforms state-of-the-art CL methods by a significant margin on a variety of datasets.

关键词： Training computer vision conferences Performance gain pattern recognition

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Learning Contextual Causality between Daily Events from Time-consecutive Images

Learning Contextual Causality between Daily Events from Time...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Hongming Huo, Yintong Zhao, Xinran Song, Yangqiu Roth, Dan HKUST Hong Kong Peoples R China CUHK Hong Kong Peoples R China UPenn Philadelphia PA 19104 USA

ISBN: (纸本)9781665448994

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists and considering the contextual property can help better predict the causal relation between events. The Vis-Causal dataset and experiment code are available at https://***/HKUST-KnowComp/Vis_Causal.

关键词： Training Visualization computer vision Annotations Knowledge acquisition conferences pattern recognition

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