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Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

Backdoor Attacks Against Deep Image Compression via Adaptive...

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

作者： Yu, Yi Wang, Yufei Yang, Wenhan Lu, Shijian Tan, Yap-Peng Kot, Alex C. Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Nanyang Technol Univ IGP ROSE Singapore Singapore

ISBN: (纸本)9798350301298

Recent deep-learning-based compression methods have achieved superior performance compared with traditional approaches. However, deep learning models have proven to be vulnerable to backdoor attacks, where some specific trigger patterns added to the input can lead to malicious behavior of the models. In this paper, we present a novel backdoor attack with multiple triggers against learned image compression models. Motivated by the widely used discrete cosine transform (DCT) in existing compression systems and standards, we propose a frequency-based trigger injection model that adds triggers in the DCT domain. In particular, we design several attack objectives for various attacking scenarios, including: 1) attacking compression quality in terms of bit-rate and reconstruction quality;2) attacking task-driven measures, such as down-stream face recognition and semantic segmentation. Moreover, a novel simple dynamic loss is designed to balance the influence of different loss terms adaptively, which helps achieve more efficient training. Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.

关键词： Low-level vision

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Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark

Visibility Constrained Wide-band Illumination Spectrum Desig...

引用

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

作者： Niu, Muyao Li, Zhuoxiao Zhong, Zhihang Zheng, Yinqiang Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350301298

Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://***/MyNiuuu/VCSD.

关键词： Physics-based vision and shape-from-X

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Transferable Adversarial Attacks on vision Transformers with Token Gradient Regularization

Transferable Adversarial Attacks on Vision Transformers with...

引用

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

作者： Zhang, Jianping Huang, Yizhan Wu, Weibin Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China

ISBN: (纸本)9798350301298

vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based attacks, our TGR offers a performance improvement of 8.8% on average.

关键词： Adversarial attack and defense

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DVIO - Distributed Visual-Inertial Odometry in a Multi-user Environment

DVIO - Distributed Visual-Inertial Odometry in a Multi-user ...

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ieee conference on Virtual Reality and 3D User Interfaces (VR)

作者： Zhang, Juyi Lutfallah, Mathieu Kunz, Andreas Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350374490;9798350374506

Head-mounted displays typically use a visual-inertial odometry system, which relies on the headset's camera combined with Inertial Measurement Units. While effective, this setup fails if the camera is obstructed or if the environments lacks features. Traditional recalibration methods like place recognition often fall short in such settings. Addressing this, the paper proposes a novel distributed tracking method that uses the positions of other users. This approach creates a network or "daisy chain" of user locations, enhancing position tracking accuracy. It serves as an alternative and a supplementary solution to the standard system, ensuring precise location tracking for all users.

关键词： Human-centered computing Human computer interaction (HCI) Interaction paradigms-Virtual reality Computing methodologies Artificial intelligence-computer vision computer vision problemsTracking

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Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

Guiding Pseudo-labels with Uncertainty Estimation for Source...

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

作者： Litrico, Mattia Del Bue, Alessio Morerio, Pietro Ist Italiano Tecnol Pattern Anal & Comp Vision PAVIS Genoa Italy

ISBN: (纸本)9798350301298

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.

关键词： continual low-shot meta or long-tail learning Transfer

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HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model

HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation ...

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

作者： Junayed, Masum Shah Sadeghzadeh, Arezoo Islam, Md Baharul Wong, Lai-Kuan Aydin, Tarkan Bahcesehir Univ Istanbul Turkey Amer Univ Malta Cospicua Malta Multimedia Univ Cyberjaya Malaysia

ISBN: (纸本)9781665487399

Monocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360 degrees surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation. Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges. The performance is further improved, benefiting from a self and cross attention layer and spatial/temporal patches in the Transformer encoder and decoder, respectively. Besides, a spatial residual block is employed to reduce the number of parameters. By jointly passing the deep features extracted from an input image at each backbone block, along with the raw depth maps predicted by the transformer encoder-decoder, through a context adjustment layer, our model can produce resulting depth maps with better visual quality than the ground-truth. Comprehensive ablation studies demonstrate the significance of each individual module. Extensive experiments conducted on three datasets;Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE can achieve state-of-the-art performance for 360 degrees monocular depth estimation. Complete project code and supplementary materials are available at https://***/himode5008/HiMODE.

关键词： Visualization Three-dimensional displays Estimation Lighting computer architecture Feature extraction Transformers

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DeCo : Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-to-Fine Contrastive Ranking

DeCo : Decomposition and Reconstruction for Compositional Te...

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

作者： Yang, Lijin Kong, Quan Yang, Hsuan-Kung Kehl, Wadim Sato, Yoichi Kobori, Norimasa Univ Tokyo Tokyo Japan Woven Toyota Tokyo Japan

ISBN: (纸本)9798350301298

Understanding dense action in videos is a fundamental challenge towards the generalization of vision models. Several works show that compositionality is key to achieving generalization by combining known primitive elements, especially for handling novel composited structures. Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding. In recent works, composition is assumed to be learned from pairs of whole videos and language embeddings through large scale self-supervised pre-training. Alternatively, one can process the video and language into word-level primitive elements, and then only learn fine-grained semantic correspondences. Both approaches do not consider the granularity of the compositions, where different query granularity corresponds to different video segments. Therefore, a good compositional representation should be sensitive to different video and query granularity. We propose a method to learn a coarse-to-fine compositional representation by decomposing the original query sentence into different granular levels, and then learning the correct correspondences between the video and recombined queries through a contrastive ranking constraint. Additionally, we run temporal boundary prediction in a coarse-to-fine manner for precise grounding boundary detection. Experiments are performed on two datasets, Charades-CG and ActivityNet-CG, showing the superior compositional generalizability of our approach.

关键词： and reasoning language vision

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WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models

WEDGE: A multi-weather autonomous driving dataset built from...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Marathe, Aboli Ramanan, Deva Walambe, Rahee Kotecha, Ketan Carnegie Mellon University Machine Learning Department PA United States Carnegie Mellon University Robotics Institute PA United States India India

ISBN: (纸本)9798350302493

The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI's terms 1 of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://***/WEDGE. © 2023 ieee.

关键词： Benchmarking

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Tri-Perspective View for vision-Based 3D Semantic Occupancy Prediction

Tri-Perspective View for Vision-Based 3D Semantic Occupancy ...

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

作者： Huang, Yuanhui Zheng, Wenzhao Zhang, Yunpeng Zhou, Jie Lu, Jiwen Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350301298

Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://***/wzzheng/TPVFormer.

关键词： Autonomous driving

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MobileViG: Graph-Based Sparse Attention for Mobile vision Applications

MobileViG: Graph-Based Sparse Attention for Mobile Vision Ap...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Munir, Mustafa Avery, William Marculescu, Radu The University of Texas at Austin United States

ISBN: (纸本)9798350302493

Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have dominated computer vision. However, recently proposed vision graph neural networks (ViG) provide a new avenue for exploration. Unfortunately, for mobile applications, ViGs are computationally expensive due to the overhead of representing images as graph structures. In this work, we propose a new graph-based sparse attention mechanism, Sparse vision Graph Attention (SVGA), that is designed for ViGs running on mobile devices. Additionally, we propose the first hybrid CNN-GNN architecture for vision tasks on mobile devices, MobileViG, which uses SVGA. Extensive experiments show that MobileViG beats existing ViG models and existing mobile CNN and ViT architectures in terms of accuracy and/or speed on image classification, object detection, and instance segmentation tasks. Our fastest model, MobileViG-Ti, achieves 75.7% top-1 accuracy on ImageNet-1K with 0.78 ms inference latency on iPhone 13 Mini NPU (compiled with CoreML), which is faster than MobileNetV2x1.4 (1.02 ms, 74.7% top-1) and MobileNetV2x1.0 (0.81 ms, 71.8% top-1). Our largest model, MobileViG-B obtains 82.6% top-1 accuracy with only 2.30 ms latency, which is faster and more accurate than the similarly sized EfficientFormer-L3 model (2.77 ms, 82.4%). Our work proves that well designed hybrid CNN-GNN architectures can be a new avenue of exploration for designing models that are extremely fast and accurate on mobile devices. Our code is publicly available at https://***/SLDGroup/MobileViG. © 2023 ieee.

关键词： Object detection

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