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- Privacy Risks of General-Purpose Language Models311
This study presents the first systematic study on the privacy risks of 8 state-of-the-art language models with 4 diverse case studies and demonstrates the aforementioned privacy risks do exist and can impose practical threats to the application of general-purpose language models on sensitive data covering identity, genome, healthcare and location.
- Dual Encoding for Zero-Example Video Retrieval305
This paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own and establishes a new state-of-the-art for zero-example video retrieval.
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- Model-Reuse Attacks on Deep Learning Systems205
It is demonstrated that malicious primitive models pose immense threats to the security of ML systems, and analytical justification for the effectiveness of model-reuse attacks is provided, which points to the unprecedented complexity of today's primitive models.
- Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion178
This paper develops automatic tools to extract expert patterns from the source code and casts the code into a semantic graph to extract deep graph features and significantly outperforms state-of-the-art methods.
- Trojaning Language Models for Fun and Profit175
This work presents TrojanLM, a new class of trojaning attacks in which maliciously crafted LMs trigger host NLP systems to malfunction in a highly predictable manner, and provides analytical justification for the practicality of TrojanLM.
- SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems159
SirenAttack is evaluated on a set of state-of-the-art deep learning-based acoustic systems (including speech command recognition, speaker recognition and sound event classification), with results showing the versatility, effectiveness, and stealthiness of SirenAttack.
- Backdoor Pre-trained Models Can Transfer to All157
A new approach to map the inputs containing triggers directly to a predefined output representation of the pre-trained NLP models, e.g., a preddefined output representation for the classification token in BERT, instead of a target label, which can introduce backdoor to a wide range of downstream tasks without any prior knowledge.
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- VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities147
This paper proposes VulSniper, which is designed to detect fine-grained vulnerabilities more effectively and achieves F1-scores of 80.6% and 73.3% on the two benchmark datasets respectively, which are significantly higher than those of the state-of-the-art methods.
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- Deep Dual Consecutive Network for Human Pose Estimation143
A novel multi-frame human pose estimation framework, leveraging abundant temporal cues between video frames to facilitate keypoint detection is proposed, which ranks No.1 in the Multi-frame Person Pose Estimation Challenge on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018.
- Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art132
This paper conducts a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks againstPE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.
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- SecGraph: a uniform and open-source evaluation system for graph data anonymization and de-anonymization117
This paper proposes and develops SecGraph, a uniform and open-source Secure Graph data sharing/publishing system that enables data owners to anonymize data by state-of-the-art anonymization techniques, measure the data's utility, and evaluate the data' vulnerability against modern De-Anonymization (DA) attacks.
- Structural Data De-anonymization112
This work quantitatively shows the conditions for perfect and (1-ε)-perfect structural data DA of 26 real world structural datasets, including Social Networks, Collaborations Networks, Communication Networks, Autonomous Systems, and Peer-to-Peer networks and designs a practical and novel single-phase cold start Optimization based DA (ODA) algorithm.
- Deep Graph Matching and Searching for Semantic Code Retrieval111
An end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval that significantly outperforms state-of-the-art baseline models by a large margin on both datasets.
- Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models107
A novel testing framework for deep learning copyright protection: DEEPJUDGE quantitatively tests the similarities between two deep learning models: a victim model and a suspect model, which leverages a diverse set of testing metrics and efficient test case generation algorithms to produce a chain of supporting evidence to help determine whether a suspects model is a copy of the victim model.
- Multilevel Graph Matching Networks for Deep Graph Similarity Learning97
A multilevel graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion that consistently outperforms state-of-the-art baseline models on both the graph–graph classification and graph– graph regression tasks.
- Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction96
A novel RNN architecture termed AHMR (Attentive Hierarchical Motion Recurrent network) for motion prediction which simultaneously models local motion contexts and a global context is proposed and achieves much enhanced long-term prediction proficiency, such as retaining natural human-like motions over 50 seconds predictions.
- On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge96
The overall structural information based de-anonymization attack is much more powerful than the seed knowledge-only based deanonymized attack, and even without any seed information, a social network can be perfectly or partially de-Anonymized.
- VeriFi: Towards Verifiable Federated Unlearning92
This work prompts the concept of verifiable federated unlearning and proposes VeriFi, a unified framework that allows systematic analysis of federated unlearning and quantification of its effect, with different combinations of various unlearning and verification methods.
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- NeuronFair81
NeuronFair is a new DNN fairness testing framework that differs from previous work in several key aspects: inter-pretable - it quantitatively interprets DNNs' fairness violations for the biased decision; effective - it uses the interpretation results to guide the generation of more diverse instances in less time; generic - it can handle both structured and unstructured data.
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- Differentially Private Releasing via Deep Generative Model75
dp-GAN provides theoretical privacy guarantee via enforcing the differential privacy principle; it retains desirable utility in the released model, enabling a variety of otherwise impossible analyses; and most importantly, it achieves practical training scalability and stability by employing multi-fold optimization strategies.
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- Adversarial CAPTCHAs71
An adversarial completely automated public turing test to tell computers and humans apart (CAPTCHA) is studied and a framework for text-based and image-based adversarial CAPTCHA generation on top of state-of-the-art adversarial image generation techniques is proposed.
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- A large-scale empirical analysis of the vulnerabilities introduced by third-party components in IoT firmware68
The first large-scale analysis of the usage of TPCs and the corresponding vulnerabilities in firmware is presented, which reveals the diversity of security issues for different kinds of firmware from various vendors, and discovers some well-known vulnerabilities are still deeply rooted in many firmware images.
- Aggregated Multi-GANs for Controlled 3D Human Motion Prediction66
A generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion, and a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented.
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- An Embarrassingly Simple Backdoor Attack on Self-supervised Learning62
It is suggested that SSL and supervised learning are comparably vulnerable to backdoor attacks, and the existing defenses against supervised backdoor attacks are not easily retrofitted to the unique vulnerability of SSL.
- Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning62
A new metric which covers both linguistic qualities and semantic informativeness based on BERT is designed which outperforms other metrics even without reference summaries and is general and transferable across datasets.
- Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network61
An Attribute-Specific Embedding Network (ASEN) is proposed to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space.
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- Attend to count: Crowd counting with adaptive capacity multi-scale CNNs57
The Adaptive Capacity Multi-scale convolutional neural networks (ACM-CNN), a novel crowd counting approach which can assign different capacities to different portions of the input, is proposed.
- Structure Based Data De-Anonymization of Social Networks and Mobility Traces57
An Unified Similarity (US) measurement is designed, based on which a US based De-Anonymization (DA) framework is presented which iteratively de-anonymizes data with an accuracy guarantee.
- AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy55
This work proposes an Adaptive Hierarchical Decomposition (AHEAD) protocol, which adaptively and dynamically controls the built tree structure, so that the injected noise is well controlled for maintaining high utility.
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- Distributed and Asynchronous Data Collection in Cognitive Radio Networks with Fairness Consideration53
- Approximation algorithms for load-balanced virtual backbone construction in wireless sensor networks51
Three NP-hard problems are investigated in the paper, namely, the MinMax Degree Maximal Independent Set (MDMIS), the Load-Balanced Virtual Backbone (LBVB) problem, and the Min Max Valid-degree non-Backbone node Allocation (MVBA) problem.
- GA-Par: Dependable Microservice Orchestration Framework for Geo-Distributed Clouds50
A new dependable microservice orchestration framework GA-Par is described to effectively select and deploy microservices whilst reducing the discrepancy between user security requirements and actual service provision and experiments show that the solution outperforms the greedy-based security aware method.
- Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning49
Extensive experiments on three fashion-related datasets, i.e., FashionAI, DARN, and DeepFashion, show the effectiveness of ASEN for fine-grained fashion similarity prediction and its potential for fashion reranking.
- Understanding the Security Risks of Docker Hub49
The first large-scale and in-depth security analysis against Docker images is conducted, uncovering multiple security-critical aspects of Docker images with an empirical but comprehensive analysis, covering sensitive parameters in run-commands, the executed programs in Docker images, and vulnerabilities in contained software.
- THP: A Novel Authentication Scheme to Prevent Multiple Attacks in SDN-Based IoT Network49
This article design, implement, and evaluate a new authentication scheme called the hidden pattern (THP), which combines graphics password and digital challenge value to prevent multiple types of authentication attacks at the same time.
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- Towards understanding the security of modern image captchas and underground captcha-solving services46
This paper proposes simple yet powerful attack frameworks against popular image captchas, identifies some design flaws in these popular schemes, and examines the underground market for captcha-solving services, identifying 152 such services.
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- A Truthful FPTAS Mechanism for Emergency Demand Response in Colocation Data Centers44
This paper formalizes the MEDR problem and proposes a dynamic programming to solve the optimization version of the problem, and design and prove that the mechanism is truthful, an FPTAS, and outperforms near-optimal and high utility.
- Fighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN)43
Through the comprehensive evaluations on public datasets, it is demonstrated that the proposed PPCNN model performs better than existing detection methods and show better generalization.
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- Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study38
This paper proposes four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs and makes the first attempt to conduct an extensive empirical study of black- box attacks against real-world cloud-based image detectors.
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- Artificial intelligence security and privacy: a survey36
This survey systematically categorizing and analyzing the threats and countermeasures in AI systems, which span both the training and inference stages, encompass centralized and distributed settings, and address both conventional and foundation AI models.
- AdvSQLi: Generating Adversarial SQL Injections Against Real-World WAF-as-a-Service36
This paper proposes a general and extendable attack framework, namely AdvSQLi, in which a minimal series of transformations are performed on the hierarchical tree representation of the original SQLi payload, such that the generated SQLi payloads can not only bypass WAF-as-a-service under black-box settings but also keep the same functionality and maliciousness as the original payload.
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- General Graph Data De-Anonymization34
A novel, robust, and effective de-anonymization attack to mobility trace data and social data is presented and a US-based De-Anonymization (DA) framework, which iteratively de- anonymizes data with accuracy guarantee is presented.
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- Load-balanced CDS construction in wireless sensor networks via genetic algorithm34
This paper proposes a novel problem the Load-Balanced CDS (LBCDS) problem, in which constructing an LBCDS and load-balancedly allocating dominatees to dominators are investigated simultaneously, and a Genetic Algorithm based strategy called LBC DS-GA is proposed to construct an CDS.
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- Detecting Missed Security Operations Through Differential Checking of Object-based Similar Paths28
Inconsistent Path Pairs as a bug Oracle is presented, a static analysis framework for detecting security bugs based on differential checking that eliminates the requirement of constructing a large number of similar code pieces, addressing the limitation of traditional cross-checking approaches.
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- Minimum-sized influential node set selection for social networks under the independent cascade model27
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- Fast and parameter-light rare behavior detection in maritime trajectories21
A scheme cross trajectories, vessel attributes and the movement context for detecting rare behaviors through preprocessing, kNN-based clustering, and verification is concerned, which shows that high accuracy to labelled anomalies and useful rare behaviors can be achieved.
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- De-Health: All Your Online Health Information Are Belong to Us19
A novel online health data De-Anonymization (DA) framework, named De-Health, is presented and the DA efficacy is validated, and a linkage attack framework is presented which can link online health/medical information to real world people.
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- Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting18
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- How IoT Re-using Threatens Your Sensitive Data: Exploring the User-Data Disposal in Used IoT Devices14
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- SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems9
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- WIP: Auditing Artist Style Pirate in Text-to-image Generation Models3
A new paradigm, called StyleAuditor, for artistic style auditing, which identifies if a suspect model has been fine-tuned using a specific artist’s artwork by analyzing style-related features and queries a trained discriminator to gain the auditing decisions.
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- GaLileo: General Linear Relaxation Framework for Tightening Robustness Certification of Transformers3
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- Attention with Long-Term Interval-Based Gated Recurrent Units for Modeling Sequential User Behaviors2
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- The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness1
The key strategy for enhancing model robustness entails the transfer of robustness from the pre-trained model to the fine-tuned model, coupled with adding a small amount of Gaussian noise to augment the representative capacity of the model.
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- Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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