Publications (88)
Revisiting the Reliability of Language Models in Instruction-Following
Jianshuo Dong, Yutong Zhang, Yan Liu +4
Advanced LLMs have achieved near-ceiling instruction-following accuracy on benchmarks such as IFEval. However, these impressive scores do not necessarily translate to reliable serv…
Speculating LLMs' Chinese Training Data Pollution from Their Tokens
Qingjie Zhang, Di Wang, Haoting Qian +7
Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-min…
An Engorgio Prompt Makes Large Language Model Babble on
Jianshuo Dong, Ziyuan Zhang, Qingjie Zhang +7
Auto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks. However, the new paradigm of these LLMs also exposes novel threats. In th…
Watermarking Pre-trained Encoders in Contrastive Learning
Yutong Wu, Han Qiu, Tianwei Zhang +2
Contrastive learning has become a popular technique to pre-train image encoders, which could be used to build various downstream classification models in an efficient way. This pro…
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey
Jiaxing Huang, Jingyi Zhang, Kai Jiang +2
Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising tw…
Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks
Jialai Wang, Ziyuan Zhang, Meiqi Wang +6
Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary could tamper with a small number of model parameter bits to break the integrity of DNNs…
An Inattention Model for Traveler Behavior with e-Coupons
Han Qiu
In this study, we consider traveler coupon redemption behavior from the perspective of an urban mobility service. Assuming traveler behavior is in accordance with the principle of…
A Unified Hardware-based Threat Detector for AI Accelerators
Xiaobei Yan, Han Qiu, Tianwei Zhang
The proliferation of AI technology gives rise to a variety of security threats, which significantly compromise the confidentiality and integrity of AI models and applications. Exis…
When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity
Shiyao Cui, Xijia Feng, Yingkang Wang +6
Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across context…
Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator
Xiaobei Yan, Xiaoxuan Lou, Guowen Xu +4
DNN accelerators have been widely deployed in many scenarios to speed up the inference process and reduce the energy consumption. One big concern about the usage of the accelerator…
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques
Han Qiu, Yi Zeng, Tianwei Zhang +2
It is extensively studied that Deep Neural Networks (DNNs) are vulnerable to Adversarial Examples (AEs). With more and more advanced adversarial attack methods have been developed,…
Survive at All Costs: Exploring LLM's Risky Behaviors under Survival Pressure
Yida Lu, Jianwei Fang, Xuyang Shao +7
As Large Language Models (LLMs) evolve from chatbots to agentic assistants, they are increasingly observed to exhibit risky behaviors when subjected to survival pressure, such as t…
SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution
Runyi Hu, Jie Zhang, Yiming Li +4
In today's digital landscape, the blending of AI-generated and authentic content has underscored the need for copyright protection and content authentication. Watermarking has beco…
Learning to Prompt Segment Anything Models
Jiaxing Huang, Kai Jiang, Jingyi Zhang +4
Segment Anything Models (SAMs) like SEEM and SAM have demonstrated great potential in learning to segment anything. The core design of SAMs lies with Promptable Segmentation, which…
Can Large Language Models Automate the Refinement of Cellular Network Specifications?
Jianshuo Dong, Yuanjie Li, Jun Liu +2
Cellular networks, e.g., 4G/5G, rely on complex technical specifications to ensure correct functionality; however, these specifications often contain flaws or ambiguities. In this…
A Case for Application-Aware Space Radiation Tolerance in Orbital Computing
Meiqi Wang, Han Qiu, Longnv Xu +5
We are witnessing a surge in the use of commercial off-the-shelf (COTS) hardware for cost-effective in-orbit computing, such as deep neural network (DNN) based on-satellite sensor…
ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection
Axel Delaval, Shujian Yang, Haicheng Wang +2
Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in French remains underdeveloped,…
An MRC Framework for Semantic Role Labeling
Nan Wang, Jiwei Li, Yuxian Meng +5
Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument label…
Stop Before You Fail: Operational Capability Boundaries for Mitigating Unproductive Reasoning in Large Reasoning Models
Qingjie Zhang, Yujia Fu, Yang Wang +5
Current answering paradigms for Large Reasoning Models (LRMs) often fail to account for the fact that some questions may lie beyond the model's operational capability boundary, lea…
Visible Yet Unreadable: A Systematic Blind Spot of Vision Language Models Across Writing Systems
Jie Zhang, Ting Xu, Gelei Deng +5
Writing is a universal cultural technology that reuses vision for symbolic communication. Humans display striking resilience: we readily recognize words even when characters are fr…
VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking
Runyi Hu, Jie Zhang, Yiming Li +4
Artificial Intelligence Generated Content (AIGC) has advanced significantly, particularly with the development of video generation models such as text-to-video (T2V) models and ima…
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction
Yi Zeng, Zihao Qi, Wencheng Chen +3
With more encrypted network traffic gets involved in the Internet, how to effectively identify network traffic has become a top priority in the field. Accurate identification of th…
The Side Effects of Being Smart: Safety Risks in MLLMs' Multi-Image Reasoning
Renmiao Chen, Yida Lu, Shiyao Cui +6
As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle complex, multi-image instructions, this advancement may pose new safety risks. We stud…
Learning Correlation Space for Time Series
Han Qiu, Hoang Thanh Lam, Francesco Fusco +1
We propose an approximation algorithm for efficient correlation search in time series data. In our method, we use Fourier transform and neural network to embed time series into a l…
jTrans: Jump-Aware Transformer for Binary Code Similarity
Hao Wang, Wenjie Qu, Gilad Katz +5
Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent s…
Leak-Resistant Unlearning: A New Benchmark for Evaluating Multi-Hop Reasoning Consistency and Recovery Robustness
Haoting Qian, Qingjie Zhang, Zhicong Huang +2
Benchmarking machine unlearning methods is critical to understand whether sensitive knowledge is removed from large language models (LLMs) or not. Current unlearning benchmarks inc…
Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge Computing
Tian Dong, Ziyuan Zhang, Han Qiu +3
Transforming off-the-shelf deep neural network (DNN) models into dynamic multi-exit architectures can achieve inference and transmission efficiency by fragmenting and distributing…
Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning
Guanlin Li, Kangjie Chen, Shangwei Guo +6
Large language models (LLMs) have emerged as powerful tools for addressing a wide range of general inquiries and tasks. Despite this, fine-tuning aligned LLMs on smaller, domain-sp…
Operationalizing AI in Future Networks: A Bird's Eye View from the System Perspective
Qiong Liu, Tianzhu Zhang, Masoud Hemmatpour +5
Modern Artificial Intelligence (AI) technologies, led by Machine Learning (ML), have gained unprecedented momentum over the past decade. Following this wave of "AI summer", the net…
SafeSearch: Automated Red-Teaming of LLM-Based Search Agents
Jianshuo Dong, Sheng Guo, Hao Wang +6
Search agents connect LLMs to the Internet, enabling them to access broader and more up-to-date information. However, this also introduces a new threat surface: unreliable search r…
Physiologically Informed Deep Learning: A Multi-Scale Framework for Next-Generation PBPK Modeling
Shunqi Liu, Han Qiu, Tong Wang
Physiologically Based Pharmacokinetic (PBPK) modeling is a cornerstone of model-informed drug development (MIDD), providing a mechanistic framework to predict drug absorption, dist…
Fingerprinting Image-to-Image Generative Adversarial Networks
Guanlin Li, Guowen Xu, Han Qiu +5
Generative Adversarial Networks (GANs) have been widely used in various application scenarios. Since the production of a commercial GAN requires substantial computational and human…
EvoVid: Temporal-Centric Self-Evolution for Video Large Language Models
Shiqi Huang, Ziyue Wang, Zhongrong Zuo +3
Recent Video Large Language Models (Video-LLMs) have demonstrated strong capabilities in video reasoning through reinforcement learning (RL). However, existing RL pipelines rely he…
System Log Parsing: A Survey
Tianzhu Zhang, Han Qiu, Gabriele Castellano +3
Modern information and communication systems have become increasingly challenging to manage. The ubiquitous system logs contain plentiful information and are thus widely exploited…
SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models
Renyang Liu, Kangjie Chen, Han Qiu +4
Image generation models (IGMs), while capable of producing impressive and creative content, often memorize a wide range of undesirable concepts from their training data, leading to…
Understanding the Dark Side of LLMs' Intrinsic Self-Correction
Qingjie Zhang, Di Wang, Haoting Qian +7
Intrinsic self-correction was proposed to improve LLMs' responses via feedback prompts solely based on their inherent capability. However, recent works show that LLMs' intrinsic se…
Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias
Rongwu Xu, Zi'an Zhou, Tianwei Zhang +5
The common toxicity and societal bias in contents generated by large language models (LLMs) necessitate strategies to reduce harm. Present solutions often demand white-box access t…
Masked AutoDecoder is Effective Multi-Task Vision Generalist
Han Qiu, Jiaxing Huang, Peng Gao +3
Inspired by the success of general-purpose models in NLP, recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers…
Prompt Ensemble Self-training for Open-Vocabulary Domain Adaptation
Jiaxing Huang, Jingyi Zhang, Han Qiu +2
Traditional domain adaptation assumes the same vocabulary across source and target domains, which often struggles with limited transfer flexibility and efficiency while handling ta…
Privacy-preserving Health Data Sharing for Medical Cyber-Physical Systems
Han Qiu, Meikang Qiu, Meiqin Liu +1
The recent spades of cyber security attacks have compromised end users' data safety and privacy in Medical Cyber-Physical Systems (MCPS). Traditional standard encryption algorithms…
An Interpretable Federated Learning-based Network Intrusion Detection Framework
Tian Dong, Song Li, Han Qiu +1
Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a…
Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun, Diyi Yang, Xiaoya Li +6
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks. However, a long-standing criticism against neural netw…
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation
Han Qiu, Yi Zeng, Shangwei Guo +3
Public resources and services (e.g., datasets, training platforms, pre-trained models) have been widely adopted to ease the development of Deep Learning-based applications. However…
Rethinking Adversarial Training with Neural Tangent Kernel
Guanlin Li, Han Qiu, Shangwei Guo +2
Adversarial training (AT) is an important and attractive topic in deep learning security, exhibiting mysteries and odd properties. Recent studies of neural network training dynamic…
Understanding the Dilemma of Unlearning for Large Language Models
Qingjie Zhang, Haoting Qian, Zhicong Huang +5
Unlearning seeks to remove specific knowledge from large language models (LLMs), but its effectiveness remains contested. On one side, "forgotten" knowledge can often be recovered…
What Makes a Good LLM Agent for Real-world Penetration Testing?
Gelei Deng, Yi Liu, Yuekang Li +5
LLM-based agents show promise for automating penetration testing, yet reported performance varies widely across systems and benchmarks. We analyze 28 LLM-based penetration testing…
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
Ziyi Lin, Chris Liu, Renrui Zhang +13
We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-languag…
Fingerprinting Multi-exit Deep Neural Network Models via Inference Time
Tian Dong, Han Qiu, Tianwei Zhang +3
Transforming large deep neural network (DNN) models into the multi-exit architectures can overcome the overthinking issue and distribute a large DNN model on resource-constrained s…
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks
Yi Zeng, Han Qiu, Gerard Memmi +1
Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wro…
The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
Rongwu Xu, Brian S. Lin, Shujian Yang +6
Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility beha…
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
Wenxuan Wang, Qiang Sun, Tao Chen +5
The recent research of facial expression recognition has made a lot of progress due to the development of deep learning technologies, but some typical challenging problems such as…
Spatial Preference Rewarding for MLLMs Spatial Understanding
Han Qiu, Peng Gao, Lewei Lu +3
Multimodal large language models~(MLLMs) have demonstrated promising spatial understanding capabilities, such as referencing and grounding object descriptions. Despite their succes…
LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
Han Qiu, Jiaxing Huang, Peng Gao +4
Hallucination, a phenomenon where multimodal large language models~(MLLMs) tend to generate textual responses that are plausible but unaligned with the image, has become one major…
Revisiting Backdoor Attacks on LLMs: A Stealthy and Practical Poisoning Framework via Harmless Inputs
Jiawei Kong, Hao Fang, Xiaochen Yang +5
Recent studies have widely investigated backdoor attacks on Large Language Models (LLMs) by inserting harmful question-answer (QA) pairs into their training data. However, we revis…
COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation
Ziyuan Zhang, Han Qiu, Maosen Zhang +4
With the rapidly increasing number of satellites in space and their enhanced capabilities, the amount of earth observation images collected by satellites is exceeding the transmiss…
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques
Qinkai Zheng, Han Qiu, Gerard Memmi +1
This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there…
Omnipotent Adversarial Training in the Wild
Guanlin Li, Kangjie Chen, Yuan Xu +2
Adversarial training is an important topic in robust deep learning, but the community lacks attention to its practical usage. In this paper, we aim to resolve a real-world challeng…
Video-KTR: Reinforcing Video Reasoning via Key Token Attribution
Ziyue Wang, Sheng Jin, Zhongrong Zuo +5
Reinforcement learning (RL) has shown strong potential for enhancing reasoning in multimodal large language models, yet existing video reasoning methods often rely on coarse sequen…
MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
Renrui Zhang, Han Qiu, Tai Wang +7
Monocular 3D object detection has long been a challenging task in autonomous driving. Most existing methods follow conventional 2D detectors to first localize object centers, and t…
BorderDet: Border Feature for Dense Object Detection
Han Qiu, Yuchen Ma, Zeming Li +2
Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted t…
CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection
Hao Wang, Zeyu Gao, Chao Zhang +4
Binary code similarity detection (BCSD) is a fundamental technique for various application. Many BCSD solutions have been proposed recently, which mostly are embedding-based, but h…
Dynamic Pricing in Shared Mobility on Demand Service
Han Qiu, Ruimin Li, Jinhua Zhao
We consider a profit maximization problem in an urban mobility on-demand service, of which the operator owns a fleet, provides both exclusive and shared trip services, and dynamica…
Mask Image Watermarking
Runyi Hu, Jie Zhang, Shiqian Zhao +5
We present MaskWM, a simple, efficient, and flexible framework for image watermarking. MaskWM has two variants: (1) MaskWM-D, which supports global watermark embedding, watermark l…
ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks
Xiaobei Yan, Han Qiu, Tianwei Zhang
Bit-flip attacks (BFAs) represent a serious threat to Deep Neural Networks (DNNs), where flipping a small number of bits in the model parameters or binary code can significantly de…
State-Dependent Safety Failures in Multi-Turn Language Model Interaction
Pengcheng Li, Jie Zhang, Tianwei Zhang +5
Safety alignment in large language models is typically evaluated under isolated queries, yet real-world use is inherently multi-turn. Although multi-turn jailbreaks are empirically…
One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training
Jianshuo Dong, Han Qiu, Yiming Li +5
Deep neural networks (DNNs) are widely deployed on real-world devices. Concerns regarding their security have gained great attention from researchers. Recently, a new weight modifi…
An Efficient Data Protection Architecture Based on Fragmentation and Encryption
Han Qiu
In this thesis, a completely revisited data protection scheme based on selective encryption is presented. First, this new scheme is agnostic in term of data format, second it has a…
BitHydra: Towards Bit-flip Inference Cost Attack against Large Language Models
Xiaobei Yan, Yiming Li, Hao Wang +2
Large language models (LLMs) are widely deployed, but their substantial compute demands make them vulnerable to inference cost attacks that aim to deliberately maximize the output…
Data Protection: Combining Fragmentation, Encryption, and Dispersion, a final report
Gerard Memmi, Katarzyna Kapusta, Patrick Lambein +1
Hardening data protection using multiple methods rather than 'just' encryption is of paramount importance when considering continuous and powerful attacks in order to observe, stea…
LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety
Junxiao Yang, Haoran Liu, Jinzhe Tu +9
Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried in low-resource languages. We a…
Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models
Shangwei Guo, Tianwei Zhang, Han Qiu +3
Watermarking has become the tendency in protecting the intellectual property of DNN models. Recent works, from the adversary's perspective, attempted to subvert watermarking mechan…
Warfare:Breaking the Watermark Protection of AI-Generated Content
Guanlin Li, Yifei Chen, Jie Zhang +5
AI-Generated Content (AIGC) is rapidly expanding, with services using advanced generative models to create realistic images and fluent text. Regulating such content is crucial to p…
LeakDojo: Decoding the Leakage Threats of RAG Systems
Maosen Zhang, Jianshuo Dong, Boting Lu +4
Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems…
DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling
Boheng Li, Junjie Wang, Yiming Li +7
Despite the integration of safety alignment and external filters, text-to-image (T2I) generative systems are still susceptible to producing harmful content, such as sexual or viole…
Towards Understanding the Cognitive Habits of Large Reasoning Models
Jianshuo Dong, Yujia Fu, Chuanrui Hu +2
Large Reasoning Models (LRMs), which autonomously produce a reasoning Chain of Thought (CoT) before producing final responses, offer a promising approach to interpreting and monito…
When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models
Haoran Ou, Kangjie Chen, Xingshuo Han +4
Large Language Models (LLMs) have been augmented with web search to overcome the limitations of the static knowledge boundary by accessing up-to-date information from the open Inte…
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques
Han Qiu, Yi Zeng, Qinkai Zheng +3
Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs). A large amount of efforts have been spent to launch and heat the arms race between the at…
Beyond Retrieval: Improving Evidence Quality for LLM-based Multimodal Fact-Checking
Haoran Ou, Gelei Deng, Xingshuo Han +4
The increasing multimodal disinformation, where deceptive claims are reinforced through coordinated text and visual content, poses significant challenges to automated fact-checking…
Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition
Wenxuan Wang, Yanwei Fu, Qiang Sun +7
Affective computing and cognitive theory are widely used in modern human-computer interaction scenarios. Human faces, as the most prominent and easily accessible features, have att…
Course-Correction: Safety Alignment Using Synthetic Preferences
Rongwu Xu, Yishuo Cai, Zhenhong Zhou +6
The risk of harmful content generated by large language models (LLMs) becomes a critical concern. This paper presents a systematic study on assessing and improving LLMs' capability…
ShieldVLM: Safeguarding the Multimodal Implicit Toxicity via Deliberative Reasoning with LVLMs
Shiyao Cui, Qinglin Zhang, Xuan Ouyang +6
Toxicity detection in multimodal text-image content faces growing challenges, especially with multimodal implicit toxicity, where each modality appears benign on its own but convey…
When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models
Cheng Wang, Gelei Deng, Xianglin Yang +2
Large Audio-Language Models (LALMs) are enhanced with audio perception capabilities, enabling them to effectively process and understand multimodal inputs that combine audio and te…
CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
Hao Wang, Zeyu Gao, Chao Zhang +7
Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and…
Privacy-preserving Collaborative Learning with Automatic Transformation Search
Wei Gao, Shangwei Guo, Tianwei Zhang +3
Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their trainin…
Your Agentic LLMs Secretly Encode Latent Signals of Indirect Prompt-Injection Exposure
Jianshuo Dong, Yiming Liu, Maosen Zhang +6
Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.g., malicious side-tasks hidden in external tool results. While many efforts have sought to address the th…
Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings
Shujian Yang, Shiyao Cui, Chuanrui Hu +5
Detecting toxic content using language models is important but challenging. While large language models (LLMs) have demonstrated strong performance in understanding Chinese, recent…
FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset
Shuhe Wang, Xiaoya Li, Jiwei Li +8
Due to the data-driven nature of current face identity (FaceID) customization methods, all state-of-the-art models rely on large-scale datasets containing millions of high-quality…
A General Framework for Defending Against Backdoor Attacks via Influence Graph
Xiaofei Sun, Jiwei Li, Xiaoya Li +5
In this work, we propose a new and general framework to defend against backdoor attacks, inspired by the fact that attack triggers usually follow a \textsc{specific} type of attack…