Search Results for author: Qing Guo

Found 75 papers, 18 papers with code

Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One

no code implementations19 Feb 2024 Tianlin Li, XiaoYu Zhang, Chao Du, Tianyu Pang, Qian Liu, Qing Guo, Chao Shen, Yang Liu

Building on this insight and observation, we develop FairThinking, a pipeline designed to automatically generate roles that enable LLMs to articulate diverse perspectives for fair expressions.

Fairness Language Modelling +1

FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution

no code implementations6 Feb 2024 Qi Zhou, Dongxia Wang, Tianlin Li, Zhihong Xu, Yang Liu, Kui Ren, Wenhai Wang, Qing Guo

To expose this potential vulnerability, we aim to build an adversarial attack forcing SDEdit to generate a specific data distribution aligned with a specified attribute (e. g., female), without changing the input's attribute characteristics.

Adversarial Attack Attribute +1

Transductive Reward Inference on Graph

no code implementations6 Feb 2024 Bohao Qu, Xiaofeng Cao, Qing Guo, Yi Chang, Ivor W. Tsang, Chengqi Zhang

In this study, we present a transductive inference approach on that reward information propagation graph, which enables the effective estimation of rewards for unlabelled data in offline reinforcement learning.


AdvGPS: Adversarial GPS for Multi-Agent Perception Attack

no code implementations30 Jan 2024 Jinlong Li, Baolu Li, Xinyu Liu, Jianwu Fang, Felix Juefei-Xu, Qing Guo, Hongkai Yu

The multi-agent perception system collects visual data from sensors located on various agents and leverages their relative poses determined by GPS signals to effectively fuse information, mitigating the limitations of single-agent sensing, such as occlusion.

Adversarial Attack object-detection +1

Spy-Watermark: Robust Invisible Watermarking for Backdoor Attack

no code implementations4 Jan 2024 Ruofei Wang, Renjie Wan, Zongyu Guo, Qing Guo, Rui Huang

Backdoor attack aims to deceive a victim model when facing backdoor instances while maintaining its performance on benign data.

Backdoor Attack backdoor defense

Leveraging Open-Vocabulary Diffusion to Camouflaged Instance Segmentation

no code implementations29 Dec 2023 Tuan-Anh Vu, Duc Thanh Nguyen, Qing Guo, Binh-Son Hua, Nhat Minh Chung, Ivor W. Tsang, Sai-Kit Yeung

Such cross-domain representations are desirable in segmenting camouflaged objects where visual cues are subtle to distinguish the objects from the background, especially in segmenting novel objects which are not seen in training.

Instance Segmentation Segmentation +1

C-NERF: Representing Scene Changes as Directional Consistency Difference-based NeRF

1 code implementation5 Dec 2023 Rui Huang, Binbin Jiang, Qingyi Zhao, William Wang, Yuxiang Zhang, Qing Guo

Our approach surpasses state-of-the-art 2D change detection and NeRF-based methods by a significant margin.

Change Detection

TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation

no code implementations3 Dec 2023 Xiaojun Jia, Jindong Gu, Yihao Huang, Simeng Qin, Qing Guo, Yang Liu, Xiaochun Cao

At the second stage, the pixels are divided into different branches based on their transferable property which is dependent on Kullback-Leibler divergence.

Adversarial Attack Image Classification +2

IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks

no code implementations18 Oct 2023 Yue Cao, Tianlin Li, Xiaofeng Cao, Ivor Tsang, Yang Liu, Qing Guo

The underlying rationale behind our idea is that image resampling can alleviate the influence of adversarial perturbations while preserving essential semantic information, thereby conferring an inherent advantage in defending against adversarial attacks.

Adversarial Robustness

SAIR: Learning Semantic-aware Implicit Representation

no code implementations13 Oct 2023 Canyu Zhang, Xiaoguang Li, Qing Guo, Song Wang

To this end, we propose a framework with two modules: (1) building a semantic implicit representation (SIR) for a corrupted image whose large regions miss.

Image Inpainting Image Reconstruction

CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification

no code implementations ICCV 2023 Rabab Abdelfattah, Qing Guo, Xiaoguang Li, XiaoFeng Wang, Song Wang

Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels.

Classification Multi-Label Image Classification +2

SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting

no code implementations26 Jul 2023 Canyu Zhang, Qing Guo, Xiaoguang Li, Renjie Wan, Hongkai Yu, Ivor Tsang, Song Wang

Given the coordinates of a pixel we want to reconstruct, we first collect its neighboring pixels in the input image and extract their detail-enhanced semantic embeddings, unmask-attentional semantic embeddings, importance values, and spatial distances to the desired pixel.

Image Inpainting Image Reconstruction +2

CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields

1 code implementation ICCV 2023 Ziyuan Luo, Qing Guo, Ka Chun Cheung, Simon See, Renjie Wan

Neural Radiance Fields (NeRF) have the potential to be a major representation of media.

CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion

no code implementations ICCV 2023 Haotian Dong, Enhui Ma, Lubo Wang, Miaohui Wang, Wuyuan Xie, Qing Guo, Ping Li, Lingyu Liang, Kairui Yang, Di Lin

In this paper, we propose Cross-View Synthesis Transformer (CVSformer), which consists of Multi-View Feature Synthesis and Cross-View Transformer for learning cross-view object relationships.


Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection

no code implementations30 Jun 2023 Huiming Sun, Lan Fu, Jinlong Li, Qing Guo, Zibo Meng, Tianyun Zhang, Yuewei Lin, Hongkai Yu

Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model.

Adversarial Attack object-detection +2

FAIRER: Fairness as Decision Rationale Alignment

no code implementations27 Jun 2023 Tianlin Li, Qing Guo, Aishan Liu, Mengnan Du, Zhiming Li, Yang Liu

Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment.


On the Robustness of Segment Anything

no code implementations25 May 2023 Yihao Huang, Yue Cao, Tianlin Li, Felix Juefei-Xu, Di Lin, Ivor W. Tsang, Yang Liu, Qing Guo

Second, we extend representative adversarial attacks against SAM and study the influence of different prompts on robustness.

Autonomous Vehicles valid

Architecture-agnostic Iterative Black-box Certified Defense against Adversarial Patches

no code implementations18 May 2023 Di Yang, Yihao Huang, Qing Guo, Felix Juefei-Xu, Ming Hu, Yang Liu, Geguang Pu

The adversarial patch attack aims to fool image classifiers within a bounded, contiguous region of arbitrary changes, posing a real threat to computer vision systems (e. g., autonomous driving, content moderation, biometric authentication, medical imaging) in the physical world.

Autonomous Driving

Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models

no code implementations18 May 2023 Yihao Huang, Felix Juefei-Xu, Qing Guo, Jie Zhang, Yutong Wu, Ming Hu, Tianlin Li, Geguang Pu, Yang Liu

Although recent personalization methods have democratized high-resolution image synthesis by enabling swift concept acquisition with minimal examples and lightweight computation, they also present an exploitable avenue for high accessible backdoor attacks.

Backdoor Attack Image Generation

Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal

no code implementations18 May 2023 Xiaoguang Li, Qing Guo, Pingping Cai, Wei Feng, Ivor Tsang, Song Wang

State-of-the-art shadow removal methods train deep neural networks on collected shadow & shadow-free image pairs, which are desired to complete two distinct tasks via shared weights, i. e., data restoration for shadow regions and identical mapping for non-shadow regions.

Image Shadow Removal Shadow Removal

Evading DeepFake Detectors via Adversarial Statistical Consistency

no code implementations CVPR 2023 Yang Hou, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Jianjun Zhao

Second, we find that the statistical differences between natural and DeepFake images are positively associated with the distribution shifting between the two kinds of images, and we propose to use a distribution-aware loss to guide the optimization of different degradations.

DeepFake Detection Face Swapping

FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning

no code implementations21 Feb 2023 Anran Li, Hongyi Peng, Lan Zhang, Jiahui Huang, Qing Guo, Han Yu, Yang Liu

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model.

Feature Importance feature selection +1

Leveraging Inpainting for Single-Image Shadow Removal

1 code implementation ICCV 2023 Xiaoguang Li, Qing Guo, Rabab Abdelfattah, Di Lin, Wei Feng, Ivor Tsang, Song Wang

In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.

Image Inpainting Image Shadow Removal +1

Structure-Informed Shadow Removal Networks

no code implementations9 Jan 2023 Yuhao Liu, Qing Guo, Lan Fu, Zhanghan Ke, Ke Xu, Wei Feng, Ivor W. Tsang, Rynson W. H. Lau

Hence, in this paper, we propose to remove shadows at the image structure level.

Shadow Removal

Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition

no code implementations CVPR 2023 Leming Guo, Wanli Xue, Qing Guo, Bo Liu, Kaihua Zhang, Tiantian Yuan, ShengYong Chen

Existing results in [9, 20, 25, 36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained.

Knowledge Distillation Sign Language Recognition

Be Careful with Rotation: A Uniform Backdoor Pattern for 3D Shape

no code implementations28 Nov 2022 Linkun Fan, Fazhi He, Qing Guo, Wei Tang, Xiaolin Hong, Bing Li

As a result, backdoor pattern designed for one certain 3D data structure will be disable for other data structures of the same 3D scene.

Backdoor Attack

Background-Mixed Augmentation for Weakly Supervised Change Detection

1 code implementation21 Nov 2022 Rui Huang, Ruofei Wang, Qing Guo, Jieda Wei, Yuxiang Zhang, Wei Fan, Yang Liu

Change detection (CD) is to decouple object changes (i. e., object missing or appearing) from background changes (i. e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc.

Change Detection Data Augmentation +1

Coarse-to-fine Task-driven Inpainting for Geoscience Images

no code implementations20 Nov 2022 Huiming Sun, Jin Ma, Qing Guo, Qin Zou, Shaoyue Song, Yuewei Lin, Hongkai Yu

To the best of our knowledge, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images by ignoring the geoscience related tasks.

Data Augmentation Image Inpainting +2

Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement

no code implementations12 Oct 2022 Shuangzhi Li, Zhijie Wang, Felix Juefei-Xu, Qing Guo, Xingyu Li, Lei Ma

Then, for the first attempt, we construct a benchmark based on the physical-aware common corruptions for point cloud detectors, which contains a total of 1, 122, 150 examples covering 7, 481 scenes, 25 common corruption types, and 6 severities.

Autonomous Driving Cloud Detection +4

DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions

no code implementations21 Sep 2022 Xuhong Ren, Jianlang Chen, Felix Juefei-Xu, Wanli Xue, Qing Guo, Lei Ma, Jianjun Zhao, ShengYong Chen

Then, we propose a novel core-failure-set guided DARTS that embeds a K-center-greedy algorithm for DARTS to select suitable corrupted failure examples to refine the model architecture.

Data Augmentation

IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection

1 code implementation15 Jul 2022 Qing Guo, Ruofei Wang, Rui Huang, Shuifa Sun, Yuxiang Zhang

Change detection (CD) aims to detect change regions within an image pair captured at different times, playing a significant role in diverse real-world applications.

Change Detection Vocal Bursts Intensity Prediction

Single Object Tracking Research: A Survey

no code implementations25 Apr 2022 Ruize Han, Wei Feng, Qing Guo, QinGhua Hu

Visual object tracking is an important task in computer vision, which has many real-world applications, e. g., video surveillance, visual navigation.

Object object-detection +4

NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks

no code implementations24 Mar 2022 Xiaofei Xie, Tianlin Li, Jian Wang, Lei Ma, Qing Guo, Felix Juefei-Xu, Yang Liu

Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs.

Defect Detection DNN Testing +1

Masked Faces with Faced Masks

no code implementations17 Jan 2022 JiaYi Zhu, Qing Guo, Felix Juefei-Xu, Yihao Huang, Yang Liu, Geguang Pu

Modern face recognition systems (FRS) still fall short when the subjects are wearing facial masks, a common theme in the age of respiratory pandemics.

Face Recognition

ALA: Naturalness-aware Adversarial Lightness Attack

no code implementations16 Jan 2022 Yihao Huang, Liangru Sun, Qing Guo, Felix Juefei-Xu, JiaYi Zhu, Jincao Feng, Yang Liu, Geguang Pu

To obtain adversarial examples with a high attack success rate, we propose unconstrained enhancement in terms of the light and shade relationship in images.

Adversarial Attack Denoising +2

Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining

1 code implementation7 Jan 2022 Qing Guo, Jingyang Sun, Felix Juefei-Xu, Lei Ma, Di Lin, Wei Feng, Song Wang

First, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively.

Data Augmentation Single Image Deraining +1

Benchmarking Shadow Removal for Facial Landmark Detection and Beyond

no code implementations27 Nov 2021 Lan Fu, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng, Yang Liu, Song Wang

The observation of this work motivates us to design a novel detection-aware shadow removal framework, which empowers shadow removal to achieve higher restoration quality and enhance the shadow robustness of deployed facial landmark detectors.

Benchmarking Blocking +2

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

no code implementations26 Nov 2021 Hua Qi, Zhijie Wang, Qing Guo, Jianlang Chen, Felix Juefei-Xu, Lei Ma, Jianjun Zhao

In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i. e., block) level.

Adversarial Relighting Against Face Recognition

no code implementations18 Aug 2021 Qian Zhang, Qing Guo, Ruijun Gao, Felix Juefei-Xu, Hongkai Yu, Wei Feng

To this end, we first propose the physical modelbased adversarial relighting attack (ARA) denoted as albedoquotient-based adversarial relighting attack (AQ-ARA).

Adversarial Attack Face Recognition

CarveNet: Carving Point-Block for Complex 3D Shape Completion

no code implementations28 Jul 2021 Qing Guo, Zhijie Wang, Felix Juefei-Xu, Di Lin, Lei Ma, Wei Feng, Yang Liu

3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e. g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds.

Data Augmentation Point Cloud Completion

Learning to Adversarially Blur Visual Object Tracking

1 code implementation ICCV 2021 Qing Guo, Ziyi Cheng, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yang Liu, Jianjun Zhao

In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i. e., adversarial blur attack (ABA).

Object Visual Object Tracking +1

AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning

no code implementations14 Jul 2021 Yihao Huang, Qing Guo, Felix Juefei-Xu, Lei Ma, Weikai Miao, Yang Liu, Geguang Pu

To this end, we first comprehensively investigate two kinds of pixel denoising methods for adversarial robustness enhancement (i. e., existing additive-based and unexplored filtering-based methods) under the loss functions of image-level and semantic-level, respectively, showing that pixel-wise filtering can obtain much higher image quality (e. g., higher PSNR) as well as higher robustness (e. g., higher accuracy on adversarial examples) than existing pixel-wise additive-based method.

Adversarial Attack Adversarial Robustness +1

JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting

1 code implementation9 Jul 2021 Qing Guo, Xiaoguang Li, Felix Juefei-Xu, Hongkai Yu, Yang Liu, Song Wang

In this paper, for the first time, we formulate image inpainting as a mix of two problems, predictive filtering and deep generation.

Image Inpainting

Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization

1 code implementation2 Jul 2021 Qing Guo, Junya Chen, Dong Wang, Yuewei Yang, Xinwei Deng, Lawrence Carin, Fan Li, Jing Huang, Chenyang Tao

Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.

Mutual Information Estimation

Sparta: Spatially Attentive and Adversarially Robust Activation

no code implementations18 May 2021 Qing Guo, Felix Juefei-Xu, Changqing Zhou, Wei Feng, Yang Liu, Song Wang

In both cases, Sparta leads to CNNs with higher robustness than the vanilla ReLU, verifying the flexibility and versatility of the proposed method.

AVA: Adversarial Vignetting Attack against Visual Recognition

no code implementations12 May 2021 Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu

Moreover, we propose the geometry-aware level-set optimization method to solve the adversarial vignetting regions and physical parameters jointly.

Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer

no code implementations11 May 2021 Lan Fu, Hongkai Yu, Felix Juefei-Xu, Jinlong Li, Qing Guo, Song Wang

As one of the state-of-the-art perception approaches, detecting the interested objects in each frame of video surveillance is widely desired by ITS.

Object object-detection +2

AdvHaze: Adversarial Haze Attack

no code implementations28 Apr 2021 Ruijun Gao, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng

We also visualize the correlation matrices, which inspire us to jointly apply different perturbations to improve the success rate of the attack.

Adversarial Attack

DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking

no code implementations23 Apr 2021 Ziyi Cheng, Xuhong Ren, Felix Juefei-Xu, Wanli Xue, Qing Guo, Lei Ma, Jianjun Zhao

Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking.

Data Augmentation Object +1

Auto-Exposure Fusion for Single-Image Shadow Removal

2 code implementations CVPR 2021 Lan Fu, Changqing Zhou, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng, Yang Liu, Song Wang

We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.

Image Shadow Removal Shadow Removal

Countering Malicious DeepFakes: Survey, Battleground, and Horizon

1 code implementation27 Feb 2021 Felix Juefei-Xu, Run Wang, Yihao Huang, Qing Guo, Lei Ma, Yang Liu

To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed.

DeepFake Detection Face Swapping +1

Sparta: Spatially Attentive and Adversarially Robust Activations

no code implementations1 Jan 2021 Qing Guo, Felix Juefei-Xu, Changqing Zhou, Lei Ma, Xiaofei Xie, Wei Feng, Yang Liu

Moreover, comprehensive evaluations have demonstrated two important properties of our method: First, superior transferability across DNNs.


Bias Field Poses a Threat to DNN-based X-Ray Recognition

no code implementations19 Sep 2020 Binyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, Shengchao Qin

Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.

Adversarial Attack

Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering

no code implementations19 Sep 2020 Yihao Huang, Felix Juefei-Xu, Qing Guo, Yang Liu, Geguang Pu

We first demonstrate that frequency-domain notch filtering, although famously shown to be effective in removing periodic noise in the spatial domain, is infeasible for our task at hand due to the manual designs required for the notch filters.

DeepFake Detection Face Swapping +2

Adversarial Exposure Attack on Diabetic Retinopathy Imagery

no code implementations19 Sep 2020 Yupeng Cheng, Felix Juefei-Xu, Qing Guo, Huazhu Fu, Xiaofei Xie, Shang-Wei Lin, Weisi Lin, Yang Liu

In this paper, we study this problem from the viewpoint of adversarial attack and identify a totally new task, i. e., adversarial exposure attack generating adversarial images by tuning image exposure to mislead the DNNs with significantly high transferability.

Adversarial Attack

Adversarial Rain Attack and Defensive Deraining for DNN Perception

no code implementations19 Sep 2020 Liming Zhai, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Lei Ma, Wei Feng, Shengchao Qin, Yang Liu

To defend the DNNs from the negative rain effect, we also present a defensive deraining strategy, for which we design an adversarial rain augmentation that uses mixed adversarial rain layers to enhance deraining models for downstream DNN perception.

Adversarial Attack Autonomous Driving +5

EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining

2 code implementations19 Sep 2020 Qing Guo, Jingyang Sun, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Wei Feng, Yang Liu

To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i. e., EfficientDeRain, which is able to process a rainy image within 10~ms (i. e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i. e., RCDNet), while achieving similar de-rain effects.

Data Augmentation Single Image Deraining

Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack

no code implementations14 Jul 2020 Yupeng Cheng, Qing Guo, Felix Juefei-Xu, Wei Feng, Shang-Wei Lin, Weisi Lin, Yang Liu

To this end, we initiate the very first attempt to study this problem from the perspective of adversarial attack and propose the adversarial denoise attack.

Adversarial Attack Common Sense Reasoning +2

FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

1 code implementation13 Jun 2020 Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu

At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image.

DeepFake Detection Face Swapping +2

DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms

no code implementations13 Jun 2020 Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, Jianjun Zhao

As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors.

DeepFake Detection Face Swapping +2

FakeLocator: Robust Localization of GAN-Based Face Manipulations

no code implementations27 Jan 2020 Yihao Huang, Felix Juefei-Xu, Qing Guo, Yang Liu, Geguang Pu

In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization.

Data Augmentation Face Generation +3

Amora: Black-box Adversarial Morphing Attack

no code implementations9 Dec 2019 Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu

In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called \textbf{\underline{a}dversarial \underline{mor}phing \underline{a}ttack} (a. k. a.

Adversarial Attack Dictionary Learning +3

SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking

1 code implementation ECCV 2020 Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, Yang Liu

We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency.

Adversarial Attack Video Object Tracking +2

Research Commentary on Recommendations with Side Information: A Survey and Research Directions

no code implementations19 Sep 2019 Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke

This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.

Knowledge Graphs Recommendation Systems +1

Effects of Blur and Deblurring to Visual Object Tracking

no code implementations21 Aug 2019 Qing Guo, Wei Feng, Zhihao Chen, Ruijun Gao, Liang Wan, Song Wang

In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers.

Deblurring Image Deblurring +1

Learning Dynamic Siamese Network for Visual Object Tracking

no code implementations ICCV 2017 Qing Guo, Wei Feng, Ce Zhou, Rui Huang, Liang Wan, Song Wang

How to effectively learn temporal variation of target appearance, to exclude the interference of cluttered background, while maintaining real-time response, is an essential problem of visual object tracking.

Object Visual Object Tracking

Cannot find the paper you are looking for? You can Submit a new open access paper.