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- STRIP1,145
This work builds STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system and focuses on vision system, which achieves an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for different types of triggers.
- Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection299
A new attacking method is introduced that generates adversarial examples of Android malware and evades being detected by the current models, and can also deceive recent machine learning-based detectors that rely on semantic features such as control-flow-graph.
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- Man-in-the-Middle Attacks Against Machine Learning Classifiers Via Malicious Generative Models46
This work explores vulnerabilities of DNN models under the umbrella of Man-in-the-Middle (MitM) attacks, which has not been investigated before, and investigates using a VAE decoder to either transform benign inputs to their adversarial counterparts or decode outputs from benign VAE encoders to be adversarial examples.
- Defending Against Adversarial Attack Towards Deep Neural Networks Via Collaborative Multi-Task Training40
A novel defensive framework based on collaborative multi-task training, aiming at providing defence for different types of attacks is proposed, which can prevent adversaries from finding valid adversarial examples when the defence strategy is exposed.
- StyleFool: Fooling Video Classification Systems via Style Transfer35
StyleFool is proposed, a black-box video adversarial attack via style transfer to fool the video classification system and outperforms the state-of-the-art adversarial attacks in terms of both the number of queries and the robustness against existing defenses.
- QUEEN: Query Unlearning Against Model Extraction31
QUEEN (QUEry unlEarNing) is proposed that proactively launches counterattacks on potential model extraction attacks from the very beginning and outperforms the state-of-the-art defenses against various model extraction attacks with a relatively low cost to the model accuracy.
- Provably Unlearnable Data Examples20
A mechanism for certifying the so-called $(q, \eta)$-Learnability of an unlearnable dataset via parametric smoothing is proposed and a simple recovery attack can restore the clean-task performance of the classifiers trained on UEs by slightly perturbing the learned weights.
- Mitigating Unauthorized Speech Synthesis for Voice Protection16
An effective, transferable, and robust proactive protection technology named Pivotal Objective Perturbation (POP) that applies imperceptible error-minimizing noises on original speech samples to prevent them from being effectively learned for text-to-speech (TTS) synthesis models so that high-quality deepfake speeches cannot be generated.
- SafeSpeech: Robust and Universal Voice Protection Against Malicious Speech Synthesis13
This work proposes a defensive framework, SafeSpeech, which protects the users' audio before uploading by embedding imperceptible perturbations on original speeches to prevent high-quality synthetic speech.
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- AI Model Modulation with Logits Redistribution9
This work proposes AIM, a novel model modulation paradigm that enables a single model to exhibit diverse behaviors to meet the specific end requirements, and introduces a logits redistribution strategy that operates in a training data-agnostic and retraining-free manner.
- Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems9
Comprehensive evaluation on four commercial ASR APIs demonstrates the protection superiority of AudioShield over existing competitors, and both objective and subjective evaluations indicate that AudioShield significantly improves the audio quality.
- Re-Key-Free, Risky-Free: Adaptable Model Usage Control8
Experiments demonstrate that AdaLoc can offer a practical solution for adaptive and protected DNN deployment in evolving real-world scenarios, and establishes a formal foundation to underpin AdaLoc, providing crucial bounds such as the errors introduced by updates restricted to the access key.
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- Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer8
Experimental results show that the proposed attack on ASR systems based on user-customized style transfer can meet the need for user-customized styles and achieve a success rate of 82% in attacks, while keeping sound naturalness due to the authors' user study.
- Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions8
The iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.
- CyberAlly: Leveraging LLMs and Knowledge Graphs to Empower Cyber Defenders7
CyberAlly is introduced, a knowledge graph-enhanced AI assistant designed to enhance the efficiency and effectiveness of Blue Teams during incident response and equipping defenders to tackle evolving threats with greater precision and confidence.
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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 2026-10-12. 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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