IEEE Transactions on Circuits and Systems for Video Technology最新文献_第8页

Enhancing transparent object matting using predicted definite foreground and background 利用预测确定的前景和背景增强透明物体的消光效果

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/tcsvt.2024.3452512

Yihui Liang, Qian Fu, Zou Kun, Guisong Liu, Han Huang

引用次数: 0

Cross-Scene Hyperspectral Image Classification With Consistency-Aware Customized Learning 利用一致性感知定制学习进行跨场景高光谱图像分类

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/tcsvt.2024.3452135

Kexin Ding, Ting Lu, Wei Fu, Leyuan Fang

引用次数: 0

Robust Generative Steganography Based on Image Mapping 基于图像映射的鲁棒生成式隐写术

IF 8.3 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/TCSVT.2024.3451620

Qinghua Zhang;Fangjun Huang

{"title":"Robust Generative Steganography Based on Image Mapping","authors":"Qinghua Zhang;Fangjun Huang","doi":"10.1109/TCSVT.2024.3451620","DOIUrl":"10.1109/TCSVT.2024.3451620","url":null,"abstract":"Coverless steganography requires no modification of the cover image and can effectively resist steganalysis, which has received widespread attention from researchers in recent years. However, existing coverless image steganographic methods are achieved by constructing a mapping between the secret information and images in a known dataset. This image dataset needs to be sent to the receiver, which consumes substantial resources and poses a risk of information leakage. In addition, existing methods cannot achieve high-accuracy extraction when facing various attacks. To address the aforementioned issues, we propose a robust generative steganography based on image mapping (GSIM). This method establishes prompts based on the topic and quantity requirements first and then generate the candidate image database according to the prompts, which can be independently generated by both the sender and receiver without the need for transmission. In order to improve the robustness of the algorithm, our proposed GSIM utilizes prompts and fractional-order Chebyshev-Fourier moments (FrCHFMs) to construct the mapping between the generated images and the predefined binary sequences, as well as uses speeded-up robust features (SURFs) as auxiliary features in the information extraction phase. The experimental results show that GSIM is superior to existing coverless image steganographic methods in terms of capacity, security, and robustness.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"34 12","pages":"13543-13555"},"PeriodicalIF":8.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion Patch Attack With Spatial–Temporal Cross-Evolution for Video Recognition 利用时空交叉进化的扩散补丁攻击进行视频识别

IF 8.3 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/TCSVT.2024.3452475

Jian Yang;Zhiyu Guan;Jun Li;Zhiping Shi;Xianglong Liu

{"title":"Diffusion Patch Attack With Spatial–Temporal Cross-Evolution for Video Recognition","authors":"Jian Yang;Zhiyu Guan;Jun Li;Zhiping Shi;Xianglong Liu","doi":"10.1109/TCSVT.2024.3452475","DOIUrl":"10.1109/TCSVT.2024.3452475","url":null,"abstract":"Deep neural networks (DNNs) have demonstrated excellent performance across various domains. However, recent studies have shown that deep neural networks are vulnerable to adversarial examples, including DNN-based video action recognition models. While much of the existing research on adversarial attacks against video models focuses on perturbation-based attacks, there is limited research on patch-based black-box attacks. Existing patch-based attack algorithms suffer from the problem of a large search space of optimization algorithms and use patches with simple content, leading to suboptimal attack performance or requiring a large number of queries. To address these challenges, we propose the “Diffusion Patch Attack (DPA) with Spatial-Temporal Cross-Evolution (STCE) for Video Recognition,” a novel approach that integrates the excellent properties of the diffusion model into video black-box adversarial attacks for the first time. This integration significantly narrows the parameter search space while enhancing the adversarial content of patches. Moreover, we introduce the spatial-temporal cross-evolutionary algorithm to adapt to the narrowed search space. Specifically, we separate the spatial and temporal parameters and then employ an alternate evolutionary strategy for each parameter type. Extensive experiments conducted on three widely used video action recognition models (C3D, NL, and TPN) and two benchmark datasets (UCF-101 and HMDB-51) demonstrate the superior performance of our approach compared to other state-of-the-art black-box patch attack algorithms.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"34 12","pages":"13190-13200"},"PeriodicalIF":8.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Masked Reference Token Supervision based Iterative Visual-language Framework for Robust Visual Grounding 基于掩码参考标记监督的迭代视觉语言框架，实现稳健的视觉接地

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/tcsvt.2024.3452418

Chunlei Wang, Wenquan Feng, Shuchang Lyu, Guangliang Cheng, Xiangtai Li, Binghao Liu, Qi Zhao

引用次数: 0

Exploring Vision-Language Foundation Model for Novel Object Captioning 探索新颖物体字幕的视觉语言基础模型

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/tcsvt.2024.3452437

Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Jianlin Feng, Hongyang Chao, Tao Mei

引用次数: 0

CVT-Track: Concentrating on Valid Tokens for One-Stream Tracking CVT-Track：集中使用有效令牌进行单流跟踪

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/tcsvt.2024.3452231

Jianan Li, Xiaoying Yuan, Haolin Qin, Ying Wang, Xincong Liu, Tingfa Xu

引用次数: 0

Joint lesion detection and classification of breast ultrasound video via a clinical knowledge-aware framework 通过临床知识感知框架对乳腺超声视频进行联合病灶检测和分类

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-30 DOI: 10.1109/tcsvt.2024.3452497

Minglei Li, Wushuang Gong, Pengfei Yan, Xiang Li, Yuchen Jiang, Hao Luo, Hang Zhou, Shen Yin

引用次数: 0

MoBox: Enhancing Video Object Segmentation with Motion-Augmented Box Supervision MoBox：利用运动增强盒监督增强视频对象分割功能

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-29 DOI: 10.1109/tcsvt.2024.3451981

Xiaomin Li, Qinghe Wang, Dezhuang Li, Mengmeng Ge, Xu Jia, You He, Huchuan Lu

引用次数: 0

Globally Deformable Information Selection Transformer for Underwater Image Enhancement 用于水下图像增强的全局可变形信息选择变换器

IF 8.4 1区工程技术

IEEE Transactions on Circuits and Systems for Video Technology Pub Date : 2024-08-29 DOI: 10.1109/tcsvt.2024.3451553

Junbin Zhuang, Yan Zheng, Baolong Guo, Yunyi Yan

引用次数: 0