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Data-free Defense of Black Box Models Against Adversarial Attacks

Data-free Defense of Black Box Models Against Adversarial At...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Gaurav Kumar Nayak Inder Khatri Ruchit Rawal Anirban Chakraborty University of Central Florida Orlando Florida USA New York University New York USA Indian Institute of Science Bangalore India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Several companies often safeguard their trained deep models (i.e. details of architecture, learnt weights, training details etc.) from third-party users by exposing them only as ‘black boxes' through APIs. Moreover, they may not even provide access to the training data due to proprietary reasons or sensitivity concerns. In this work, we propose a novel defense mechanism for black box models against adversarial attacks in a data-free set up. We construct synthetic data via a generative model and train surrogate network using model stealing techniques. To minimize adversarial contamination on perturbed samples, we propose ‘wavelet noise remover' (WNR) that performs discrete wavelet decomposition on input images and carefully select only a few important coefficients determined by our ‘wavelet coefficient selection module' (WCSM). To recover the high-frequency content of the image after noise removal via WNR, we further train a ‘regenerator' network with an objective to retrieve the coefficients such that the reconstructed image yields similar to original predictions on the surrogate model. At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy. Our method improves the adversarial accuracy on CIFAR-10 by 38.98% and 32.01% against the state-of-the-art Auto Attack compared to baseline, even when the attacker uses surrogate architecture (Alexnet-half and Alexnet) similar to the black box architecture (Alexnet) with same model stealing strategy as defender.

关键词： Training Accuracy Sensitivity Noise Training data computer architecture Predictive models

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The Flag Manifold as a Tool for Analyzing and Comparing Sets of Data Sets 18

The Flag Manifold as a Tool for Analyzing and Comparing Sets...

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18th ieee/CVF International conference on computer vision (ICCV)

作者： Ma, Xiaofeng Kirby, Michael Peterson, Chris Colorado State Univ Dept Math Ft Collins CO 80523 USA

ISBN: (纸本)9781665401913

The shape and orientation of data clouds reflect variability in observations that can confound pattern recognition systems. Subspace methods, utilizing Grassmann manifolds, have been a great aid in dealing with such variability. However, this usefulness begins to falter when the data cloud contains sufficiently many outliers corresponding to stray elements from another class or when the number of data points is larger than the number of features. We illustrate how nested subspace methods, utilizing flag manifolds, can help to deal with such additional confounding factors. Flag manifolds, which are parameter spaces for nested sequences of subspaces, are a natural geometric generalization of Grassmann manifolds. We utilize and extend known algorithms for determining the minimal length geodesic, the initial direction generating the minimal length geodesic, and the distance between any pair of points on a flag manifold. The approach is illustrated in the context of (hyper) spectral imagery showing the impact of ambient dimension, sample dimension, and flag structure.

关键词： Manifolds Geometry computer vision Shape conferences Data visualization Tools

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A Single Residual Network with ESA Modules and Distillation

A Single Residual Network with ESA Modules and Distillation

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yucong Wang Minjie Cai College of Computer Science and Electronic Engineering Hunan University

Although there are many methods based on deep learning that have superior performance on single image super-resolution (SISR), it is difficult to run in real time on devices with limited computing power. Some recent studies have found that simply relying on reducing parameters or reducing the theoretical FLOPs of the model does not speed up the inference time of the network in a practical sense. Actual speed on the device is probably a better measure than FLOPs. In this work, we propose a new single residual network (SRN). On the one hand, we try to introduce and optimize an attention mechanism module to improve the performance of the network with a relatively small speed loss. On the other hand, we find that residuals in residual blocks do not have a positive impact on networks with adjusted ESA. Therefore, the residual of the network residual block is removed, which not only improves the speed of the network, but also improves the performance of the network. Finally, we reduced the number of channels and the number of residual blocks of the classic model EDSR, and removed the last convolution before the long residual. We set this tuned EDSR as the teacher model and our newly proposed SRN as the student model. Under the joint effect of the original loss and the distillation loss, the performance of the network can be improved without losing the inference time. Combining the above strategies, our proposed model runs much faster than similarly performing models. As an example, we built a Fast and Efficient Network (SRN) and its small version SRN-S, which run 30%-37% faster than the state-of-the-art EISR model: a paper champion RLFN. Furthermore, the shallow version of SRN-S achieves the second-shortest inference time as well as the second-smallest number of activations in the NTIRE2023 challenge. Code will be available at https://***/wnxbwyc/SRN.

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ieee ICIP 2023 Challenge on the Automatic Detection of Mosquito Breeding Grounds

IEEE ICIP 2023 Challenge on the Automatic Detection of Mosqu...

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2023 ieee International conference on Image Processing Challenges and workshops, ICIPCW 2023

作者： Passos, Wesley L. Araujo, Gabriel M. Haque, Ubydul Cruz-Roldan, Fernando Netto, Sergio L. Federal Center for Technological Education Brazil Rutgers University United States Federal University of Rio de Janeiro Brazil University of Alcalá Spain

ISBN: (纸本)9798350302585

Every year, millions of people are infected with diseases transmitted by the Aedes aegypti mosquito. Preventing vector reproduction by identifying and eliminating possible breeding grounds is still the primary method to combat these diseases. In this challenge, we present a comprehensive dataset of aerial videos containing potential mosquito breeding sites acquired using drones. All objects of interest were manually annotated with bounding boxes: discarded tires, water tanks, buckets, puddles, pools, and bottles. Proposed systems shall locate these items at both frame and individual-object levels. Participants from three different countries (namely Brazil, Qatar, and Spain) submitted results, which may be compared to previous ones found in the related literature. © 2023 ieee.

关键词： computer vision

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How Suboptimal is Training rPPG Models with Videos and Targets from Different Body Sites?

How Suboptimal is Training rPPG Models with Videos and Targe...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Björn Braun Daniel McDuff Christian Holz Department of Computer Science ETH Zürich Switzerland Paul G. Allen School of Computer Science University of Washington

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Remote camera measurement of the blood volume pulse via photoplethysmography (rPPG) is a compelling technology for scalable, low-cost, and accessible assessment of cardiovascular information. Neural networks currently provide the state-of-the-art for this task and supervised training or fine-tuning is an important step in creating these models. However, most current models are trained on facial videos using contact PPG measurements from the fingertip as targets/labels. One of the reasons for this is that few public datasets to date have incorporated contact PPG measurements from the face. Yet there is copious evidence that the PPG signals at different sites on the body have very different morphological features. Is training a facial video rPPG model using contact measurements from another site on the body suboptimal? Using a recently released unique dataset with synchronized contact PPG and video measurements from both the hand and face, we can provide precise and quantitative answers to this question. We obtain up to 40% lower mean squared errors between the waveforms of the predicted and the ground truth PPG signals using state-of-the-art neural models when using PPG signals from the forehead compared to using PPG signals from the fingertip. We also show qualitatively that the neural models learn to predict the morphology of the ground truth PPG signal better when trained on the forehead PPG signals. However, while models trained from the forehead PPG produce a more faithful waveform, models trained from a finger PPG do still learn the dominant frequency (i.e., the heart rate) well.

关键词： Training Wrist Forehead Current measurement Volume measurement Estimation Predictive models

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Selective Multi-View Deep Model for 3D Object Classification

Selective Multi-View Deep Model for 3D Object Classification

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mona Alzahrani Muhammad Usman Saeed Anwar Tarek Helmy Department of Information & Computer Science KFUPM Dhahran Saudi Arabia College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Dhahran Saudi Arabia Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

3D object classification has emerged as a practical technology with applications in various domains, such as medical image analysis, automated driving, intelligent robots, and crowd surveillance. Among the different approaches, multi-view representations for 3D object classification have shown the most promising results, achieving state-of-the-art performance. However, there are certain limitations in current view-based 3D object classification methods. One observation is that using all captured views for classification can confuse the classifier and lead to misleading results for certain classes. Additionally, some views may contain more discriminative information for object classification than others. These observations motivate the development of smarter and more efficient selective multi-view classification models. In this work, we propose a Selective Multi-View Deep Model that extracts multi-view images from 3D data representations and selects the most influential view by assigning importance scores using the cosine similarity method based on visual features detected by a pre-trained CNN. The proposed method is evaluated on the ModelNet40 dataset for the task of 3D classification. The results demonstrate that the proposed model achieves an overall accuracy of 88.13% using only a single view when employing a shading technique for rendering the views, pre-trained ResNet-152 as the backbone CNN for feature extraction, and a Fully Connected Network (FCN) as the classifier.

关键词： Training Solid modeling Visualization Three-dimensional displays Image analysis Surveillance Feature extraction

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Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report

Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challen...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marcos V. Conde Manuel Kolmet Tim Seizinger Tom E. Bishop Radu Timofte Xiangyu Kong Dafeng Zhang Jinlong Wu Fan Wang Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Zhiguo Cao Ke Xian Chaowei Liu Zigeng Chen Xingyi Yang Songhua Liu Yongcheng Jing Michael Bi Mi Xinchao Wang Zhihao Yang Wenyi Lian Siyuan Lai Haichuan Zhang Trung Hoang Amirsaeed Yazdani Vishal Monga Ziwei Luo Fredrik K. Gustafsson Zheng Zhao Jens Sjölund Thomas B. Schön Yuxuan Zhao Baoliang Chen Yiqing Xu JiXiangNiu Computer Vision Lab University of Würzburg Germany Glass Imaging Inc CA

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical *** aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens *** the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation.

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Towards Robust Classification Model by Counterfactual and Invariant Data Generation

Towards Robust Classification Model by Counterfactual and In...

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

作者： Chang, Chun-Hao Adam, George Alexandru Goldenberg, Anna Univ Toronto Hosp Sick Children Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions. Spuriousness occurs when some features correlate with labels but are not causal;relying on such features prevents models from generalizing to unseen environments where such correlations break. In this work, we focus on image classification and propose two data generation processes to reduce spuriousness. Given human annotations of the subset of the features responsible (causal) for the labels (e.g. bounding boxes), we modify this causal set to generate a surrogate image that no longer has the same label (i.e. a counterfactual image). We also alter non-causal features to generate images still recognized as the original labels, which helps to learn a model invariant to these features. In several challenging datasets, our data generations outperform state-of-the-art methods in accuracy when spurious correlations break, and increase the saliency focus on causal features providing better explanations.

关键词： Industries computer vision Correlation Image recognition Annotations Computational modeling Machine learning

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EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

EyePAD++: A Distillation-based approach for joint Eye Authen...

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Dhar, Prithviraj Kumar, Amit Kaplan, Kirsten Gupta, Khushi Ranjan, Rakesh Chellappa, Rama Johns Hopkins University United States Meta Reality Labs

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform authentication and Presentation Attack Detection (PAD) independently and b) involve significant pre-processing steps to extract the iris region. Here, we introduce a joint framework for EA and PAD using periocular images. While a deep Multitask Learning (MTL) network can perform both the tasks, MTL suffers from the forgetting effect since the training datasets for EA and PAD are disjoint. To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting. To further improve the EA performance, we introduce a novel approach called EyePAD++ that includes training an MTL network on both EA and PAD data, while distilling the 'versatility' of the EyePAD network through an additional distillation step. Our proposed methods outperform the SOTA in PAD and obtain near-SOTA performance in eye-to-eye verification, without any pre-processing. We also demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification with PAD across network backbones and image quality. © 2022 ieee.

关键词： Training Performance evaluation Image quality Iris Protocols Machine vision Image edge detection

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Omnimatte: Associating Objects and Their Effects in Video

Omnimatte: Associating Objects and Their Effects in Video

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

作者： Lu, Erika Cole, Forrester Dekel, Tali Zisserman, Andrew Freeman, William T. Rubinstein, Michael Google Res Mountain View CA 94043 USA Univ Oxford Oxford England Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

computer vision is increasingly effective at segmenting objects in images and videos;however, scene effects related to the objects-shadows, reflections, generated smoke, etc.-are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assist a variety of applications such as removing, duplicating, or enhancing objects in video. In this work, we take a step towards solving this novel problem of automatically associating objects with their effects in video. Given an ordinary video and a rough segmentation mask over time of one or more subjects of interest, we estimate an omnimatte for each subject-an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Our model is trained only on the input video in a self-supervised manner, without any manual labels, and is generic-it produces omnimattes automatically for arbitrary objects and a variety of effects. We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semitransparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.

关键词： Training Image segmentation computer vision Color Visual effects Reflection pattern recognition

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