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Effective Ambiguity Attack Against Passport-based DNN Intellectual Property Protection Schemes through Fully Connected Layer Substitution

Effective Ambiguity Attack Against Passport-based DNN Intell...

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

作者： Chen, Yiming Tian, Jinyu Chen, Xiangyu Zhou, Jiantao Univ Macau State Key Lab Internet Things Smart City Dept Comp & Informat Sci Taipa Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Since training a deep neural network (DNN) is costly, the well-trained deep models can be regarded as valuable intellectual property (IP) assets. The IP protection associated with deep models has been receiving increasing attentions in recent years. Passport-based method, which replaces normalization layers with passport layers, has been one of the few protection solutions that are claimed to be secure against advanced attacks. In this work, we tackle the issue of evaluating the security of passport-based IP protection methods. We propose a novel and effective ambiguity attack against passport-based method, capable of successfully forging multiple valid passports with a small training dataset. This is accomplished by inserting a specially designed accessory block ahead of the passport parameters. Using less than 10% of training data, with the forged passport, the model exhibits almost indistinguishable performance difference (less than 2%) compared with that of the authorized passport. In addition, it is shown that our attack strategy can be readily generalized to attack other IP protection methods based on watermark embedding. Directions for potential remedy solutions are also given.

关键词： accountability ethics in vision fairness privacy Transparency

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Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration

Bitstream-Corrupted JPEG Images are Restorable: Two-stage Co...

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

作者： Liu, Wenyang Wang, Yi Yap, Kim-Hui Chau, Lap-Pui Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9798350301298

In this paper, we study a real-world JPEG image restoration problem with bit errors on the encrypted bitstream. The bit errors bring unpredictable color casts and block shifts on decoded image contents, which cannot be resolved by existing image restoration methods mainly relying on pre-defined degradation models in the pixel domain. To address these challenges, we propose a robust JPEG decoder, followed by a two-stage compensation and alignment framework to restore bitstream-corrupted JPEG images. Specifically, the robust JPEG decoder adopts an error-resilient mechanism to decode the corrupted JPEG bitstream. The two-stage framework is composed of the self-compensation and alignment (SCA) stage and the guided-compensation and alignment (GCA) stage. The SCA adaptively performs block-wise image color compensation and alignment based on the estimated color and block offsets via image content similarity. The GCA leverages the extracted low-resolution thumbnail from the JPEG header to guide full-resolution pixel-wise image restoration in a coarse-to-fine manner. It is achieved by a coarse-guided pix2pix network and a refine-guided bi-directional Laplacian pyramid fusion network. We conduct experiments on three benchmarks with varying degrees of bit error rates. Experimental results and ablation studies demonstrate the superiority of our proposed method. The code will be released at https://***/wenyang001/Two- ACIR.

关键词： Low-level vision

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

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

作者： Arriaga, Octavio Palacio, Sebastian Valdenegro-Toro, Matias Univ Bremen Bremen Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany Univ Groningen Groningen Netherlands

ISBN: (纸本)9798350302493

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

关键词： Object detection

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Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Out-of-Distributed Semantic Pruning for Robust Semi-Supervis...

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

作者： Wang, Yu Qiao, Pengchong Liu, Chang Song, Guoli Zheng, Xiawu Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Peking Univ AI Sci AI4S Preferred Program Shenzhen Grad Sch Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://***/rain305f/OSP.

关键词： detection recognition: Categorization retrieval

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Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?

Cap4Video: What Can Auxiliary Captions Do for Text-Video Ret...

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

作者： Wu, Wenhao Luo, Haipeng Fang, Bo Wang, Jingdong Ouyang, Wanli Univ Sydney Sydney NSW Australia Baidu Inc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Most existing text-video retrieval methods focus on cross-modal matching between the visual content of videos and textual query sentences. However, in real-world scenarios, online videos are often accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This insight has motivated us to propose a novel approach to text-video retrieval, where we directly generate associated captions from videos using zero-shot video captioning with knowledge from web-scale pre-trained models (e.g., CLIP and GPT-2). Given the generated captions, a natural question arises: what benefits do they bring to text-video retrieval? To answer this, we introduce Cap4Video, a new framework that leverages captions in three ways: i) Input data: video-caption pairs can augment the training data. ii) Intermediate feature interaction: we perform cross-modal feature interaction between the video and caption to produce enhanced video representations. iii) Output score: the Query-Caption matching branch can complement the original Query-Video matching branch for text-video retrieval. We conduct comprehensive ablation studies to demonstrate the effectiveness of our approach. Without any post-processing, Cap4Video achieves state-of-the-art performance on four standard text-video retrieval benchmarks: MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%). The code is available at https:// ***/whwu95/Cap4Video.

关键词： and reasoning language vision

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Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses

Unsupervised Contour Tracking of Live Cells by Mechanical an...

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

作者： Jang, Junbong Lee, Kwonmoo Kim, Tae-Kyun Korea Adv Inst Sci & Technol Seoul South Korea Harvard Med Sch Boston Childrens Hosp Boston MA 02115 USA Imperial Coll London London England

ISBN: (纸本)9798350301298

Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://***/JunbongJang/ contour-tracking/

关键词： cell microscopy Medical and biological vision

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SmartAssign: Learning A Smart Knowledge Assignment Strategy for Deraining and Desnowing

SmartAssign: Learning A Smart Knowledge Assignment Strategy ...

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

作者： Wang, Yinglong Mai, Chao Liu, Jianzhuang Huawei Noahs Ark Lab Montreal PQ Canada Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350301298

Existing methods mainly handle single weather types. However, the connections of different weather conditions at deep representation level are usually ignored. These connections, if used properly, can generate complementary representations for each other to make up insufficient training data, obtaining positive performance gains and better generalization. In this paper, we focus on the very correlated rain and snow to explore their connections at deep representation level. Because sub-optimal connections may cause negative effect, another issue is that if rain and snow are handled in a multi-task learning way, how to find an optimal connection strategy to simultaneously improve deraining and desnowing performance. To build desired connection, we propose a smart knowledge assignment strategy, called SmartAssign, to optimally assign the knowledge learned from both tasks to a specific one. In order to further enhance the accuracy of knowledge assignment, we propose a novel knowledge contrast mechanism, so that the knowledge assigned to different tasks preserves better uniqueness. The inherited inductive biases usually limit the modelling ability of CNNs, we introduce a novel transformer block to constitute the backbone of our network to effectively combine long-range context dependency and local image details. Extensive experiments on seven benchmark datasets verify that proposed SmartAssign explores effective connection between rain and snow, and improves the performances of both deraining and desnowing apparently. The implementation code will be available at https:// ***/ mindspore/models/tree/master/ research/cv/SmartAssign.

关键词： Low-level vision

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Boost vision Transformer with GPU-Friendly Sparsity and Quantization

Boost Vision Transformer with GPU-Friendly Sparsity and Quan...

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

作者： Yu, Chong Chen, Tao Gan, Zhongxue Fan, Jiayuan Fudan Univ Acad Engn & Technol Shanghai Peoples R China NVIDIA Corp Taipei Taiwan Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The transformer extends its success from the language to the vision domain. Because of the stacked self-attention and cross-attention blocks, the acceleration deployment of vision transformer on GPU hardware is challenging and also rarely studied. This paper thoroughly designs a compression scheme to maximally utilize the GPU-friendly 2:4 fine-grained structured sparsity and quantization. Specially, an original large model with dense weight parameters is first pruned into a sparse one by 2:4 structured pruning, which considers the GPU's acceleration of 2:4 structured sparse pattern with FP16 data type, then the floating-point sparse model is further quantized into a fixed-point one by sparse-distillation-aware quantization aware training, which considers GPU can provide an extra speedup of 2:4 sparse calculation with integer tensors. A mixed-strategy knowledge distillation is used during the pruning and quantization process. The proposed compression scheme is flexible to support supervised and unsupervised learning styles. Experiment results show GPUSQ-ViT scheme achieves state-of-the-art compression by reducing vision transformer models 6.4-12.7x on model size and 30.3-62x on FLOPs with negligible accuracy degradation on ImageNet classification, COCO detection and ADE20K segmentation benchmarking tasks. Moreover, GPUSQ-ViT can boost actual deployment performance by 1.39-1.79x and 3.22-3.43x of latency and throughput on A100 GPU, and 1.57-1.69x and 2.11-2.51x improvement of latency and throughput on AGX Orin.

关键词： Efficient and scalable vision

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Text with Knowledge Graph Augmented Transformer for Video Captioning

Text with Knowledge Graph Augmented Transformer for Video Ca...

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

作者： Gu, Xin Chen, Guang Wang, Yufei Zhang, Libo Luo, Tiejian Wen, Longyin Univ Chinese Acad Sci Beijing Peoples R China ByteDance Inc San Jose CA USA Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9798350301298

Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for real-world applications, mainly due to the long-tail words challenge. In this paper, we propose a text with knowledge graph augmented transformer (TextKG) for video captioning. Notably, TextKG is a two-stream transformer, formed by the external stream and internal stream. The external stream is designed to absorb additional knowledge, which models the interactions between the additional knowledge, e.g., pre-built knowledge graph, and the built-in information of videos, e.g., the salient object regions, speech transcripts, and video captions, to mitigate the long-tail words challenge. Meanwhile, the internal stream is designed to exploit the multi-modality information in videos (e.g., the appearance of video frames, speech transcripts, and video captions) to ensure the quality of caption results. In addition, the cross attention mechanism is also used in between the two streams for sharing information. In this way, the two streams can help each other for more accurate results. Extensive experiments conducted on four challenging video captioning datasets, i.e., YouCookII, ActivityNet Captions, MSR-VTT, and MSVD, demonstrate that the proposed method performs favorably against the state-of-the-art methods. Specifically, the proposed TextKG method outperforms the best published results by improving 18.7% absolute CIDEr scores on the YouCookII dataset.

关键词： language reasoning vision

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Investigating the Role of Attribute Context in vision-Language Models for Object recognition and Detection

Investigating the Role of Attribute Context in Vision-Langua...

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

作者： Buettner, Kyle Kovashka, Adriana Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9798350318920;9798350318937

vision-language alignment learned from image-caption pairs has been shown to benefit tasks like object recognition and detection. Methods are mostly evaluated in terms of how well object class names are learned, but captions also contain rich attribute context that should be considered when learning object alignment. It is unclear how methods use this context in learning, as well as whether models succeed when tasks require attribute and object understanding. To address this gap, we conduct extensive analysis of the role of attributes in vision-language models. We specifically measure model sensitivity to the presence and meaning of attribute context, gauging influence on object embeddings through unsupervised phrase grounding and classification via description methods. We further evaluate the utility of attribute context in training for open-vocabulary object detection, fine-grained text-region retrieval, and attribution tasks. Our results show that attribute context can be wasted when learning alignment for detection, attribute meaning is not adequately considered in embeddings, and describing classes by only their attributes is ineffective. A viable strategy that we find to increase benefits from attributes is contrastive training with adjective-based negative captions.

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures vision + language and/or other modalities

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