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Works505 from public data
- ZOO2,281
An effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN is proposed, sparing the need for training substitute models and avoiding the loss in attack transferability.
- Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!1,427
It is suggested that fine-tuning aligned LLMs introduces new safety risks that current safety infrastructures fall short of addressing -- even if a model's initial safety alignment is impeccable, it is not necessarily to be maintained after custom fine- Tuning.
- DBA: Distributed Backdoor Attacks against Federated Learning1,003
The distributed backdoor attack (DBA) is proposed --- a novel threat assessment framework developed by fully exploiting the distributed nature of FL that can evade two state-of-the-art robust FL algorithms against centralized backdoors.
- EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples701
The authors' elastic-net attacks to DNNs (EAD) feature L1-oriented adversarial examples and include the state-of-the-art L2 attack as a special case, suggesting novel insights on leveraging L1 distortion in adversarial machine learning and security implications ofDNNs.
- Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective552
A novel gradient-based attack method is presented that facilitates the difficulty of tackling discrete graph data and yields higher robustness against both different gradient based and greedy attack methods without sacrificing classification accuracy on original graph.
- Variational Quantum Circuits for Deep Reinforcement Learning483
This work reshapes classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits, and uses a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks.
- AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks460
Experimental results suggest that, by applying AutoZOOM to a state-of-the-art black-box attack (ZOO), a significant reduction in model queries can be achieved without sacrificing the attack success rate and the visual quality of the resulting adversarial examples.
- Adversarial T-Shirt! Evading Person Detectors in a Physical World448
This is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts and shows that the proposed method achieves74% and 57% attack success rates in the digital and physical worlds respectively against YOLOv2 and Faster R-CNN.
- 445
- Is Robustness the Cost of Accuracy? – A Comprehensive Study on the Robustness of 18 Deep Image Classification Models424
This paper thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models, and reveals several new insights.
- Vision Transformers Are Robust Learners404
This work uses six different diverse ImageNet datasets concerning robust classification to conduct a comprehensive performance comparison of ViT models and SOTA convolutional neural networks (CNNs), Big-Transfer (Kolesnikov et al. 2020) and presents analyses that provide both quantitative and qualitative indications to explain why ViTs are indeed more robust learners.
- A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications356
This article provides a comprehensive review of Zeroth-order optimization, with an emphasis on showing the underlying intuition, optimization principles, and recent advances in convergence analysis.
- 316
- Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?297
Ring-A-Bell is introduced, a model-agnostic red-teaming tool for T2I diffusion models that can be prepared in advance without prior knowledge of the target model, and can transform prompts that were originally regarded as safe to evade existing safety mechanisms, thus revealing the defects of the so-called safety mechanisms.
- Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples269
This paper proposes a projected gradient method combined with group lasso and gradient regularization for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities.
- 257
- Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts211
This work proposes Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism, and demonstrates the efficacy of the P4D tool in uncovering new vulnerabilities of SD models with safety mechanisms.
- 206
- Reinforcement-Learning Based Portfolio Management with Augmented Asset Movement Prediction States202
Experiments on two real-world datasets validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits and extensive simulations are conducted to demonstrate the importance of the proposed state augmentation.
- 196
- Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition175
An in-depth study of different quantum circuit encoder architectures is conducted to provide insights into designing QCNN-based feature extractors and demonstrates a high correlation between the proposed QCNN features, class activation maps, and the input Mel-spectrogram.
- Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases172
A data-limited TrojanNet detector (TND) is proposed, which can detect a TrojanNet without accessing any data samples, and it is shown that such a TND can be built by leveraging the internal response of hidden neurons, which exhibits the Trojan behavior even at random noise inputs.
- Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark163
This study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance, and introduces novel enhancements to ZO optimization, including block-wise descent, hybrid training, and gradient sparsity.
- How to Backdoor Diffusion Models?153
This paper presents the first study on the robustness of diffusion models against backdoor attacks, and proposes BadDiffusion, a novel attack framework that engineers compromised diffusion processes during model training for backdoor implantation.
- 147
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 2026-10-14. 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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