Search Results for author: Weiming Zhang

Found 83 papers, 40 papers with code

Sociolectal Analysis of Pretrained Language Models

no code implementations EMNLP 2021 Sheng Zhang, Xin Zhang, Weiming Zhang, Anders Søgaard

Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes.

AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?

2 code implementations2 Nov 2024 Benlong Wu, Guoqiang Chen, Kejiang Chen, Xiuwei Shang, Jiapeng Han, Yanru He, Weiming Zhang, Nenghai Yu

Penetration testing is essential to ensure Web security, which can detect and fix vulnerabilities in advance, and prevent data leakage and serious consequences.

FAMSeC: A Few-shot-sample-based General AI-generated Image Detection Method

no code implementations17 Oct 2024 Juncong Xu, Yang Yang, Han Fang, Honggu Liu, Weiming Zhang

The explosive growth of generative AI has saturated the internet with AI-generated images, raising security concerns and increasing the need for reliable detection methods.

Contrastive Learning

A Closer Look at Machine Unlearning for Large Language Models

1 code implementation10 Oct 2024 Xiaojian Yuan, Tianyu Pang, Chao Du, Kejiang Chen, Weiming Zhang, Min Lin

Specifically, the behavior that untargeted unlearning attempts to approximate is unpredictable and may involve hallucinations, and existing regularization is insufficient for targeted unlearning.

Diversity Machine Unlearning +1

Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate

1 code implementation9 Oct 2024 Qidong Huang, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Jiaqi Wang, Dahua Lin, Weiming Zhang, Nenghai Yu

We present the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs).

cross-modal alignment Visual Question Answering

GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models

1 code implementation22 Aug 2024 Kunsheng Tang, Wenbo Zhou, Jie Zhang, Aishan Liu, Gelei Deng, Shuai Li, Peigui Qi, Weiming Zhang, Tianwei Zhang, Nenghai Yu

By offering a realistic assessment and tailored reduction of gender biases, we hope that our GenderCARE can represent a significant step towards achieving fairness and equity in LLMs.

counterfactual Data Augmentation +2

GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation

no code implementations17 Aug 2024 Weiming Zhang, Yexin Liu, Xu Zheng, Lin Wang

The `out-of-the-box' insight of GoodSAM++ is to introduce a teacher assistant (TA) to provide semantic information for SAM, integrated with SAM to obtain reliable pseudo semantic maps to bridge both domain and capacity gaps.

Domain Adaptation Instance Segmentation +2

UniForensics: Face Forgery Detection via General Facial Representation

no code implementations26 Jul 2024 Ziyuan Fang, Hanqing Zhao, Tianyi Wei, Wenbo Zhou, Ming Wan, Zhanyi Wang, Weiming Zhang, Nenghai Yu

On the basis of the representation learned in the first stage, the second stage involves fine-tuning on face forgery detection dataset to build a deepfake detector.

Contrastive Learning DeepFake Detection +2

OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person

no code implementations23 Jul 2024 Ke Sun, Jian Cao, Qi Wang, Linrui Tian, Xindi Zhang, Lian Zhuo, Bang Zhang, Liefeng Bo, Wenbo Zhou, Weiming Zhang, Daiheng Gao

Specifically, these models struggle to maintain a balance between control and consistency when generating images for virtual clothing trials.

Virtual Try-on

DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation

no code implementations20 Jun 2024 Deguo Xia, Weiming Zhang, Xiyan Liu, Wei zhang, Chenting Gong, Jizhou Huang, Mengmeng Yang, Diange Yang

This paper overcomes these limitations and presents an industrial-grade solution named DuMapNet that outputs standardized, vectorized map elements and their topology in an end-to-end paradigm.

Any360D: Towards 360 Depth Anything with Unlabeled 360 Data and Möbius Spatial Augmentation

no code implementations19 Jun 2024 Zidong Cao, Jinjing Zhu, Weiming Zhang, Lin Wang

For this, we conduct a large suite of experiments that consider the key properties of 360 images, e. g., different 360 representations, various spatial transformations, and diverse indoor and outdoor scenes.

Rank-based No-reference Quality Assessment for Face Swapping

no code implementations4 Jun 2024 Xinghui Zhou, Wenbo Zhou, Tianyi Wei, Shen Chen, Taiping Yao, Shouhong Ding, Weiming Zhang, Nenghai Yu

Extensive experiments confirm the superiority of our method over existing general no-reference image quality assessment metrics and the latest metric of facial image quality assessment, making it well suited for evaluating face swapping images in real-world scenarios.

Face Swapping NR-IQA

©Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model

no code implementations18 Apr 2024 Chao Zhou, Huishuai Zhang, Jiang Bian, Weiming Zhang, Nenghai Yu

To mitigate this, we propose the \copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination.

Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment

no code implementations10 Apr 2024 Yexin Liu, Weiming Zhang, Athanasios V. Vasilakos, Lin Wang

Specifically, to address the first challenge, we propose a pseudo-label correction strategy that utilizes a Beta Mixture Model to predict the probability of mis-clustering based network's memory effect and rectifies the correspondence by adding a perceptual term to contrastive learning.

Clustering Contrastive Learning +4

Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models

1 code implementation CVPR 2024 Zijin Yang, Kai Zeng, Kejiang Chen, Han Fang, Weiming Zhang, Nenghai Yu

To address this issue, we propose Gaussian Shading, a diffusion model watermarking technique that is both performance-lossless and training-free, while serving the dual purpose of copyright protection and tracing of offending content.

Denoising

Provably Secure Disambiguating Neural Linguistic Steganography

1 code implementation26 Mar 2024 Yuang Qi, Kejiang Chen, Kai Zeng, Weiming Zhang, Nenghai Yu

SyncPool does not change the size of the candidate pool or the distribution of tokens and thus is applicable to provably secure language steganography methods.

Linguistic steganography

GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation

no code implementations CVPR 2024 Weiming Zhang, Yexin Liu, Xu Zheng, Lin Wang

To this end, we propose a novel framework, called GoodSAM, that introduces a teacher assistant (TA) to provide semantic information, integrated with SAM to generate ensemble logits to achieve knowledge transfer.

Domain Adaptation Instance Segmentation +3

Towards Generalist Prompting for Large Language Models by Mental Models

no code implementations28 Feb 2024 Haoxiang Guan, Jiyan He, Shuxin Zheng, En-Hong Chen, Weiming Zhang, Nenghai Yu

MeMo distills the cores of various prompting methods into individual mental models and allows LLMs to autonomously select the most suitable mental models for the problem, achieving or being near to the state-of-the-art results on diverse tasks such as STEM, logical reasoning, and commonsense reasoning in zero-shot settings.

Logical Reasoning

Model X-ray:Detecting Backdoored Models via Decision Boundary

no code implementations27 Feb 2024 Yanghao Su, Jie Zhang, Ting Xu, Tianwei Zhang, Weiming Zhang, Nenghai Yu

By accessing the model to obtain hard labels, we construct decision boundaries within the convex combination of three samples.

Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges

no code implementations27 Dec 2023 Qingyao Li, Lingyue Fu, Weiming Zhang, Xianyu Chen, Jingwei Yu, Wei Xia, Weinan Zhang, Ruiming Tang, Yong Yu

Solving the problems encountered by students poses a significant challenge for traditional deep learning models, as it requires not only a broad spectrum of subject knowledge but also the ability to understand what constitutes a student's individual difficulties.

Question Answering

Control Risk for Potential Misuse of Artificial Intelligence in Science

1 code implementation11 Dec 2023 Jiyan He, Weitao Feng, Yaosen Min, Jingwei Yi, Kunsheng Tang, Shuai Li, Jie Zhang, Kejiang Chen, Wenbo Zhou, Xing Xie, Weiming Zhang, Nenghai Yu, Shuxin Zheng

In this study, we aim to raise awareness of the dangers of AI misuse in science, and call for responsible AI development and use in this domain.

Data-Free Hard-Label Robustness Stealing Attack

1 code implementation10 Dec 2023 Xiaojian Yuan, Kejiang Chen, Wen Huang, Jie Zhang, Weiming Zhang, Nenghai Yu

In response to these identified gaps, we introduce a novel Data-Free Hard-Label Robustness Stealing (DFHL-RS) attack in this paper, which enables the stealing of both model accuracy and robustness by simply querying hard labels of the target model without the help of any natural data.

OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

2 code implementations CVPR 2024 Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, Nenghai Yu

Based on the observation, OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue, along with a rollback strategy that retrospects the presence of summary tokens in the previously generated tokens, and re-allocate the token selection if necessary.

Hallucination

Improving Adversarial Transferability by Stable Diffusion

no code implementations18 Nov 2023 Jiayang Liu, Siyu Zhu, Siyuan Liang, Jie Zhang, Han Fang, Weiming Zhang, Ee-Chien Chang

Various techniques have emerged to enhance the transferability of adversarial attacks for the black-box scenario.

Segue: Side-information Guided Generative Unlearnable Examples for Facial Privacy Protection in Real World

no code implementations24 Oct 2023 Zhiling Zhang, Jie Zhang, Kui Zhang, Wenbo Zhou, Weiming Zhang, Nenghai Yu

To address these concerns, researchers are actively exploring the concept of ``unlearnable examples", by adding imperceptible perturbation to data in the model training stage, which aims to prevent the model from learning discriminate features of the target face.

Face Recognition

HairCLIPv2: Unifying Hair Editing via Proxy Feature Blending

1 code implementation ICCV 2023 Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu

Even though they can enable very fine-grained local control, such interaction modes are inefficient for the editing conditions that can be easily specified by language descriptions or reference images.

Attribute

CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

1 code implementation5 Sep 2023 Lingyue Fu, Huacan Chai, Shuang Luo, Kounianhua Du, Weiming Zhang, Longteng Fan, Jiayi Lei, Renting Rui, Jianghao Lin, Yuchen Fang, Yifan Liu, Jingkuan Wang, Siyuan Qi, Kangning Zhang, Weinan Zhang, Yong Yu

With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers.

Code Generation Multiple-choice

Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

no code implementations10 Jul 2023 Yexin Liu, Weiming Zhang, Guoyang Zhao, Jinjing Zhu, Athanasios Vasilakos, Lin Wang

we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation.

Scene Understanding Semantic Segmentation +1

DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection

no code implementations21 May 2023 Xiao Yu, Yuang Qi, Kejiang Chen, Guoqiang Chen, Xi Yang, Pengyuan Zhu, Xiuwei Shang, Weiming Zhang, Nenghai Yu

Then, the similarity between the candidate text and the regenerated text is used as a detection feature, thus eliminating the prompt in the detection process, which allows the detector to focus on the intrinsic characteristics of the generative model.

Language Modelling Large Language Model +2

Watermarking Text Generated by Black-Box Language Models

1 code implementation14 May 2023 Xi Yang, Kejiang Chen, Weiming Zhang, Chang Liu, Yuang Qi, Jie Zhang, Han Fang, Nenghai Yu

To allow third-parties to autonomously inject watermarks into generated text, we develop a watermarking framework for black-box language model usage scenarios.

Adversarial Robustness Language Modelling +2

Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

1 code implementation CVPR 2023 Chang Liu, Weiming Zhang, Xiangru Lin, Wei zhang, Xiao Tan, Junyu Han, Xiaomao Li, Errui Ding, Jingdong Wang

It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.

Dense Object Detection Object +3

Diversity-Aware Meta Visual Prompting

1 code implementation CVPR 2023 Qidong Huang, Xiaoyi Dong, Dongdong Chen, Weiming Zhang, Feifei Wang, Gang Hua, Nenghai Yu

We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.

Diversity Visual Prompting

Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network

1 code implementation20 Feb 2023 Xiaojian Yuan, Kejiang Chen, Jie Zhang, Weiming Zhang, Nenghai Yu, Yang Zhang

At first, a top-n selection strategy is proposed to provide pseudo-labels for public data, and use pseudo-labels to guide the training of the cGAN.

Generative Adversarial Network Pseudo Label

Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks

1 code implementation17 Feb 2023 Xu Zheng, Yexin Liu, Yunfan Lu, Tongyan Hua, Tianbo Pan, Weiming Zhang, DaCheng Tao, Lin Wang

Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign) of the intensity changes.

Deblurring Deep Learning +6

VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum

no code implementations9 Jan 2023 Huanyu Bian, Zhilong Jia, Menghan Dou, Yuan Fang, Lei LI, Yiming Zhao, Hanchao Wang, Zhaohui Zhou, Wei Wang, Wenyu Zhu, Ye Li, Yang Yang, Weiming Zhang, Nenghai Yu, Zhaoyun Chen, Guoping Guo

Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.

Quantum Machine Learning Unity

CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet

1 code implementation12 Dec 2022 Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Shuyang Gu, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu

Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory.

X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion

2 code implementations7 Dec 2022 Hanqing Zhao, Dianmo Sheng, Jianmin Bao, Dongdong Chen, Dong Chen, Fang Wen, Lu Yuan, Ce Liu, Wenbo Zhou, Qi Chu, Weiming Zhang, Nenghai Yu

We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable.

Data Augmentation Instance Segmentation +5

Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion

no code implementations29 Nov 2022 Kui Zhang, Hang Zhou, Jie Zhang, Qidong Huang, Weiming Zhang, Nenghai Yu

Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving.

Autonomous Driving Denoising

PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition

no code implementations16 Sep 2022 Qidong Huang, Xiaoyi Dong, Dongdong Chen, Hang Zhou, Weiming Zhang, Kui Zhang, Gang Hua, Nenghai Yu

Notwithstanding the prominent performance achieved in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations.

MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining

no code implementations CVPR 2023 Xiaoyi Dong, Jianmin Bao, Yinglin Zheng, Ting Zhang, Dongdong Chen, Hao Yang, Ming Zeng, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu

Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language.

Representation Learning

Bootstrapped Masked Autoencoders for Vision BERT Pretraining

1 code implementation14 Jul 2022 Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, Nenghai Yu

The first design is motivated by the observation that using a pretrained MAE to extract the features as the BERT prediction target for masked tokens can achieve better pretraining performance.

Decoder Object Detection +2

Invertible Mask Network for Face Privacy-Preserving

no code implementations19 Apr 2022 Yang Yang, Yiyang Huang, Ming Shi, Kejiang Chen, Weiming Zhang, Nenghai Yu

Then, put the "Mask" face onto the protected face and generate the masked face, in which the masked face is indistinguishable from "Mask" face.

Privacy Preserving

Shape-invariant 3D Adversarial Point Clouds

1 code implementation CVPR 2022 Qidong Huang, Xiaoyi Dong, Dongdong Chen, Hang Zhou, Weiming Zhang, Nenghai Yu

In this paper, we propose a novel Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility of point perturbations.

Self-supervised Transformer for Deepfake Detection

no code implementations2 Mar 2022 Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Weiming Zhang, Nenghai Yu

After pre-training with our method, the model will then be partially fine-tuned for deepfake detection task.

Contrastive Learning DeepFake Detection +3

Protecting Celebrities from DeepFake with Identity Consistency Transformer

1 code implementation CVPR 2022 Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Ting Zhang, Weiming Zhang, Nenghai Yu, Dong Chen, Fang Wen, Baining Guo

In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions.

Face Swapping

Tracing Text Provenance via Context-Aware Lexical Substitution

no code implementations15 Dec 2021 Xi Yang, Jie Zhang, Kejiang Chen, Weiming Zhang, Zehua Ma, Feng Wang, Nenghai Yu

Tracing text provenance can help claim the ownership of text content or identify the malicious users who distribute misleading content like machine-generated fake news.

Optical Character Recognition (OCR) Sentence

Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition

no code implementations19 Oct 2021 Haozhe Chen, Weiming Zhang, Kunlin Liu, Kejiang Chen, Han Fang, Nenghai Yu

As an effective method for intellectual property (IP) protection, model watermarking technology has been applied on a wide variety of deep neural networks (DNN), including speech classification models.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression

1 code implementation18 Aug 2021 Zhaoyang Jia, Han Fang, Weiming Zhang

To address such limitations, we proposed a novel end-to-end training architecture, which utilizes Mini-Batch of Real and Simulated JPEG compression (MBRS) to enhance the JPEG robustness.

Poison Ink: Robust and Invisible Backdoor Attack

1 code implementation5 Aug 2021 Jie Zhang, Dongdong Chen, Qidong Huang, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, Nenghai Yu

As the image structure can keep its semantic meaning during the data transformation, such trigger pattern is inherently robust to data transformations.

Backdoor Attack Data Poisoning

Exploring Structure Consistency for Deep Model Watermarking

no code implementations5 Aug 2021 Jie Zhang, Dongdong Chen, Jing Liao, Han Fang, Zehua Ma, Weiming Zhang, Gang Hua, Nenghai Yu

However, little attention has been devoted to the protection of DNNs in image processing tasks.

Data Augmentation

CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows

6 code implementations CVPR 2022 Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, Baining Guo

By further pretraining on the larger dataset ImageNet-21K, we achieve 87. 5% Top-1 accuracy on ImageNet-1K and high segmentation performance on ADE20K with 55. 7 mIoU.

Image Classification Semantic Segmentation

E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion

2 code implementations15 Apr 2021 Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Weiming Zhang, Lu Yuan, Gang Hua, Nenghai Yu

This paper studies the problem of StyleGAN inversion, which plays an essential role in enabling the pretrained StyleGAN to be used for real image editing tasks.

Face Parsing

Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication

no code implementations9 Apr 2021 Xiquan Guan, Huamin Feng, Weiming Zhang, Hang Zhou, Jie Zhang, Nenghai Yu

Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding watermarking information by histogram shift.

Model Compression

Deep Model Intellectual Property Protection via Deep Watermarking

1 code implementation8 Mar 2021 Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, Nenghai Yu

By jointly training the target model and watermark embedding, the extra barrier can even be absorbed into the target model.

Multi-attentional Deepfake Detection

1 code implementation CVPR 2021 Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, Nenghai Yu

Most of them model deepfake detection as a vanilla binary classification problem, i. e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake).

Binary Classification Data Augmentation +2

Adversarial Examples Detection beyond Image Space

1 code implementation23 Feb 2021 Kejiang Chen, Yuefeng Chen, Hang Zhou, Chuan Qin, Xiaofeng Mao, Weiming Zhang, Nenghai Yu

To detect both few-perturbation attacks and large-perturbation attacks, we propose a method beyond image space by a two-stream architecture, in which the image stream focuses on the pixel artifacts and the gradient stream copes with the confidence artifacts.

Identity-Driven DeepFake Detection

no code implementations7 Dec 2020 Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Dong Chen, Fang Wen, Baining Guo

Our approach takes as input the suspect image/video as well as the target identity information (a reference image or video).

DeepFake Detection Face Swapping

LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks

no code implementations1 Nov 2020 Hang Zhou, Dongdong Chen, Jing Liao, Weiming Zhang, Kejiang Chen, Xiaoyi Dong, Kunlin Liu, Gang Hua, Nenghai Yu

To overcome these shortcomings, this paper proposes a novel label guided adversarial network (LG-GAN) for real-time flexible targeted point cloud attack.

Passport-aware Normalization for Deep Model Protection

1 code implementation NeurIPS 2020 Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu

Only when the model IP is suspected to be stolen by someone, the private passport-aware branch is added back for ownership verification.

Model Compression

GreedyFool: Distortion-Aware Sparse Adversarial Attack

1 code implementation NeurIPS 2020 Xiaoyi Dong, Dongdong Chen, Jianmin Bao, Chuan Qin, Lu Yuan, Weiming Zhang, Nenghai Yu, Dong Chen

Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by only perturbing a few pixels.

Adversarial Attack

Worst-Case-Aware Curriculum Learning for Zero and Few Shot Transfer

1 code implementation23 Sep 2020 Sheng Zhang, Xin Zhang, Weiming Zhang, Anders Søgaard

Multi-task transfer learning based on pre-trained language encoders achieves state-of-the-art performance across a range of tasks.

Transfer Learning

Model Watermarking for Image Processing Networks

1 code implementation25 Feb 2020 Jie Zhang, Dong-Dong Chen, Jing Liao, Han Fang, Weiming Zhang, Wenbo Zhou, HAO CUI, Nenghai Yu

In this way, when the attacker trains one surrogate model by using the input-output pairs of the target model, the hidden watermark will be learned and extracted afterward.

Self-supervised Adversarial Training

1 code implementation15 Nov 2019 Kejiang Chen, Hang Zhou, Yuefeng Chen, Xiaofeng Mao, Yuhong Li, Yuan He, Hui Xue, Weiming Zhang, Nenghai Yu

Recent work has demonstrated that neural networks are vulnerable to adversarial examples.

Self-Supervised Learning

Reversible Adversarial Attack based on Reversible Image Transformation

no code implementations6 Nov 2019 Zhaoxia Yin, Hua Wang, Li Chen, Jie Wang, Weiming Zhang

In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise.

Adversarial Attack Image Restoration

Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once

no code implementations ICCV 2019 Jiangfan Han, Xiaoyi Dong, Ruimao Zhang, Dong-Dong Chen, Weiming Zhang, Nenghai Yu, Ping Luo, Xiaogang Wang

Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods.

Classification General Classification

Neural Machine Reading Comprehension: Methods and Trends

no code implementations2 Jul 2019 Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang

Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years.

Machine Reading Comprehension Survey

DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense

1 code implementation ICCV 2019 Hang Zhou, Kejiang Chen, Weiming Zhang, Han Fang, Wenbo Zhou, Nenghai Yu

We propose a Denoiser and UPsampler Network (DUP-Net) structure as defenses for 3D adversarial point cloud classification, where the two modules reconstruct surface smoothness by dropping or adding points.

Denoising Point Cloud Classification

Emerging Applications of Reversible Data Hiding

no code implementations7 Nov 2018 Dongdong Hou, Weiming Zhang, Jiayang Liu, Siyan Zhou, Dong-Dong Chen, Nenghai Yu

Reversible data hiding (RDH) is one special type of information hiding, by which the host sequence as well as the embedded data can be both restored from the marked sequence without loss.

CAAD 2018: Iterative Ensemble Adversarial Attack

no code implementations7 Nov 2018 Jiayang Liu, Weiming Zhang, Nenghai Yu

Deep Neural Networks (DNNs) have recently led to significant improvements in many fields.

Adversarial Attack

CAAD 2018: Powerful None-Access Black-Box Attack Based on Adversarial Transformation Network

no code implementations3 Nov 2018 Xiaoyi Dong, Weiming Zhang, Nenghai Yu

In this paper, we propose an improvement of Adversarial Transformation Networks(ATN) to generate adversarial examples, which can fool white-box models and black-box models with a state of the art performance and won the 2rd place in the non-target task in CAAD 2018.

Unauthorized AI cannot Recognize Me: Reversible Adversarial Example

no code implementations1 Nov 2018 Jiayang Liu, Weiming Zhang, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma

In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples.

Adversarial Attack BIG-bench Machine Learning +3

Detection based Defense against Adversarial Examples from the Steganalysis Point of View

no code implementations CVPR 2019 Jiayang Liu, Weiming Zhang, Yiwei Zhang, Dongdong Hou, Yujia Liu, Hongyue Zha, Nenghai Yu

Moreover, secondary adversarial attacks cannot be directly performed to our method because our method is not based on a neural network but based on high-dimensional artificial features and FLD (Fisher Linear Discriminant) ensemble.

Steganalysis

Enhanced Attacks on Defensively Distilled Deep Neural Networks

no code implementations16 Nov 2017 Yujia Liu, Weiming Zhang, Shaohua Li, Nenghai Yu

In this paper, we first propose the epsilon-neighborhood attack, which can fool the defensively distilled networks with 100% success rate in the white-box setting, and it is fast to generate adversarial examples with good visual quality.

Face Recognition General Classification +2

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