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Found 16 papers, 9 papers with code
Jailbreaking Safeguarded Text-to-Image Models via Large Language Models
Text-to-Image models may generate harmful content, such as pornographic images, particularly when unsafe prompts are submitted.
Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning
Our findings reveal, for the first time, that even if data in the redundant set is solely used before model training, its pruning-phase membership status can still be detected through attacks.
Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning
Machine unlearning--enabling a trained model to forget specific data--is crucial for addressing biased data and adhering to privacy regulations like the General Data Protection Regulation (GDPR)'s "right to be forgotten".
RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning
This approach effectively guides the model through complex multi-hop questions with chains of related facts.
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
In the induction stage, the LLM is fed with few-shot normal reference samples and then summarizes these normal patterns to induce a set of rules for detecting anomalies.
Ranked #2 on
Video Anomaly Detection
on UCSD Ped2
PLeak: Prompt Leaking Attacks against Large Language Model Applications
As a result, a natural attack, called prompt leaking, is to steal the system prompt from an LLM application, which compromises the developer's intellectual property.
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness
Our analysis, conducted across multiple unlearning benchmarks, reveals that these algorithms inconsistently fulfill their unlearning commitments due to two main issues: 1) unlearning new data can significantly affect the unlearning utility of previously requested data, and 2) approximate algorithms fail to ensure equitable unlearning utility across different groups.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
SneakyPrompt: Jailbreaking Text-to-image Generative Models
Text-to-image generative models such as Stable Diffusion and DALL$\cdot$E raise many ethical concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones.
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Federated learning (FL) allows multiple clients to collaboratively train a deep learning model.
EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation
Skin lesions can be an early indicator of a wide range of infectious and other diseases.
Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise their effective deployment in the clinic.
Practical Blind Membership Inference Attack via Differential Comparisons
The success of the former heavily depends on the quality of the shadow model, i. e., the transferability between the shadow and the target; the latter, given only blackbox probing access to the target model, cannot make an effective inference of unknowns, compared with MI attacks using shadow models, due to the insufficient number of qualified samples labeled with ground truth membership information.
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
PatchAttack induces misclassifications by superimposing small textured patches on the input image.
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems
Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60. 2%.
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.
