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A Watermarking-Based Framework for Protecting Deep Image Classifiers Against Adversarial Attacks

A Watermarking-Based Framework for Protecting Deep Image Cla...

引用

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

作者： Sun, Chen Yang, En-Hui Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781665448994

Although deep learning-based models have achieved tremendous success in image-related tasks, they are known to be vulnerable to adversarial examples-inputs with imperceptible, but subtly crafted perturbation which fool the models to produce incorrect outputs. To distinguish adversarial examples from benign images, in this paper, we propose a novel watermarking-based framework for protecting deep image classifiers against adversarial attacks. The proposed framework consists of a watermark encoder, a possible adversary, and a detector followed by a deep image classifier to be protected. Specific methods of watermarking and detection are also presented. It is shown by experiment on a subset of ImageNet validation dataset that the proposed framework along with the presented methods of watermarking and detection is effective against a wide range of advanced attacks (static and adaptive), achieving a near zero (effective) false negative rate for FGSM and PGD attacks (static and adaptive) with the guaranteed zero false positive rate. In addition, for all tested deep image classifiers (ResNet50V2, MobileNetV2, InceptionV3), the impact of watermarking on classification accuracy is insignificant with, on average, 0.63% and 0.49% degradation in top 1 and top 5 accuracy, respectively.

关键词： Degradation computer vision Perturbation methods conferences Watermarking Detectors pattern recognition

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Layer Importance Estimation with Imprinting for Neural Network Quantization

Layer Importance Estimation with Imprinting for Neural Netwo...

引用

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

作者： Liu, Hongyang Elkerdawy, Sara Ray, Nilanjan Elhoushi, Mostafa Univ Alberta Edmonton AB Canada Huawei Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Neural network quantization has achieved a high compression rate using fixed low bit-width representation of weights and activations while maintaining the accuracy of the high-precision original network. However, mixed precision (per-layer bit-width precision) quantization requires careful tuning to maintain accuracy while achieving further compression and higher granularity than fixed-precision quantization. We propose an accuracy-aware criterion to quantify the layer's importance rank. Our method applies imprinting per layer which acts as a proxy module for accuracy estimation in an efficient way. We rank the layers based on the accuracy gain from previous modules and iteratively quantize first those with less accuracy gain. Previous mixed-precision methods either rely on expensive search techniques such as reinforcement learning (RL) or end-to-end optimization with a lack of interpretation to the quantization configuration scheme. Our method is a one-shot, efficient, accuracy-aware information estimation and thus draws better interpretability to the selected bit-width configuration.

关键词： Training Quantization (signal) Search methods Neural networks Estimation Reinforcement learning pattern recognition

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S3Net: A Single Stream Structure for Depth Guided Image Relighting

S3Net: A Single Stream Structure for Depth Guided Image Reli...

引用

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

作者： Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Asustek Comp Inc ASUS Intelligent Cloud Serv Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781665448994

Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map. To the best of our knowledge, this task is a new challenge that has not been addressed in the previous literature. To address this issue, we propose a deep learning-based neural Single Stream Structure network called S3Net for depth guided image relighting. This network is an encoder-decoder model. We concatenate all images and corresponding depth maps as the input and feed them into the model. The decoder part contains the attention module and the enhanced module to focus on the relighting-related regions in the guided images. Experiments performed on challenging benchmark show that the proposed model achieves the 3rd highest SSIM in the NTIRE 2021 Depth Guided Any-to-any Relighting Challenge.

关键词： Training Fuses Lighting Streaming media Feature extraction Decoding pattern recognition

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SRKTDN: Applying Super Resolution Method to Dehazing Task

SRKTDN: Applying Super Resolution Method to Dehazing Task

引用

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

作者： Chen, Tianyi Fu, Jiahui Jiang, Wentao Gao, Chen Liu, Si Beihang Univ Shenyuan Honors Coll Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781665448994

Nonhomogeneous haze removal is a challenging problem, which does not follow the physical scattering model of haze. Numerous existing methods focus on homogeneous haze removal by generating transmission map of the image, which is not suitable for nonhomogeneous dehazing tasks. Some methods use end-to-end model but are also designed for homogeneous haze. Inspired by Knowledge Transfer Dehazing Network and Trident Dehazing Network, we propose a model with super-resolution method and knowledge transfer method. Our model consists of a teacher network, a dehaze network and a super-resolution network. The teacher network provides the dehaze network with reliable prior, the dehaze network focuses primarily on haze removal, and the super-resolution network is used to capture details in the hazy image. Ablation study shows that the super-resolution network has significant benefit to image quality. And comparison shows that our model outperforms previous state-of-the-art methods in terms of perceptual quality on NTIRE2021 NonHomogeneous Dehazing Challenge dataset, and also performs well on other datasets.

关键词： Knowledge engineering Image quality Computational modeling conferences Superresolution Scattering pattern recognition

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Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image recognition

Incremental Meta-Learning via Episodic Replay Distillation f...

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

作者： Wang, Kai Liu, Xialei Bagdanov, Andy Herranz, Luis Jui, Shangling van de Weijer, Joost Comp Vis Ctr Barcelona Spain Nankai Univ Tianjin Peoples R China Univ Florence MICC Florence Italy Huawei Kirin Solut Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more generalizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively.

关键词： Training computer vision Image recognition Upper bound conferences pattern recognition Task analysis

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Leveraging Style and Content features for Text Conditioned Image Retrieval

Leveraging Style and Content features for Text Conditioned I...

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

作者： Chawla, Pranit Jandial, Surgan Badjatiya, Pinkesh Chopra, Ayush Sarkar, Mausoom Krishnamurthy, Balaji IIT Kharagpur Kharagpur W Bengal India IIT Hyderabad Kandi Telangana India Adobe Media & Data Sci Res Lab San Jose CA USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Image Search is a fundamental task playing a significant role in the success of wide variety of frameworks and applications. However, with the increasing sizes of product catalogues and the number of attributes per product, it has become difficult for users to express their needs effectively. Therefore, we focus on the problem of Image Retrieval with Text Feedback, which involves retrieving modified images according to the natural language feedback provided by users. In this work, we hypothesise that since an image can be delineated by its content and style features, modifications to the image can also take place in the two sub spaces respectively. Hence, we decompose an input image into its corresponding style and content features, apply modification of the text feedback individually in both the style and content spaces and finally fuse them for retrieval. Our experiments show that our approach outperforms a recent state of the art method in this task, TIRG, that seeks to use a single vector in contrast to leveraging the modification via text over style and content spaces separately.

关键词： computer vision Fuses conferences Image retrieval Natural languages pattern recognition Task analysis

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Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Extracurricular Learning: Knowledge Transfer Beyond Empirica...

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

作者： Pouransari, Hadi Javaheripi, Mojan Sharma, Vinay Tuzel, Oncel Apple Cupertino CA 95014 USA UCSD La Jolla CA USA

ISBN: (纸本)9781665448994

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions;(2) sampling examples from an approximation to the underlying data distribution;and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46 % to 68 %. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.

关键词： Training Uncertainty Neural networks Estimation Data models pattern recognition Task analysis

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Person-independent 3D Gaze Estimation using Face Frontalization 29

Person-independent 3D Gaze Estimation using Face Frontalizat...

引用

29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Jeni, Laszlo A. Cohn, Jeffrey F. Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9781509014378

Person-independent and pose-invariant estimation of eye-gaze is important for situation analysis and for automated video annotation. We propose a fast cascade regression based method that first estimates the location of a dense set of markers and their visibility, then reconstructs face shape by fitting a part-based 3D model. Next, the reconstructed 3D shape is used to estimate a canonical view of the eyes for 3D gaze estimation. The model operates in a feature space that naturally encodes local ordinal properties of pixel intensities leading to photometric invariant estimation of gaze. To evaluate the algorithm in comparison with alternative approaches, three publicly-available databases were used, Boston University Head Tracking, Multi-View Gaze and CAVE Gaze datasets. Precision for head pose and gaze averaged 4 degrees or less for pitch, yaw, and roll. The algorithm outperformed alternative methods in both datasets.

关键词： Three dimensional computer graphics

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Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

Unpaired Real-World Super-Resolution with Pseudo Controllabl...

引用

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

作者： Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781665487399

Current super-resolution methods rely on the bicubic down-sampling assumption in order to develop the ill-posed reconstruction of the low-resolution image. Not surprisingly, these approaches fail when using real-world low-resolution images due to the presence of artifacts and intrinsic noise absent in the bicubic setup. Consequently, attention is increasingly paid to techniques that alleviate this problem and super-resolve real-world images. As acquiring paired real-world datasets is a challenging problem, real-world super-resolution solutions are traditionally tackled as a blind problem or as an unpaired data-driven problem. The former makes assumptions about the down-sampling operations, the latter uses unpaired training to learn the real distributions. Recently, blind approaches have dominated this problem by assuming a diverse bank of degradations, whereas the unpaired solutions have shown under-performance due to the two-staged training. In this paper, we propose an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

关键词： Training Degradation computer vision conferences Superresolution Image restoration pattern recognition

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GRAFIQS: Face Image Quality Assessment Using Gradient Magnitudes

GRAFIQS: Face Image Quality Assessment Using Gradient Magnit...

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

作者： Kolf, Jan Niklas Damer, Naser Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350365474

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.

关键词： Biometrics computer vision Face Image Quality Assessment Face recognition

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