CLEAR: Character Unlearning in Textual and Visual Modalities
Machine Unlearning (MU) is critical for enhancing privacy and security in deep learning models, particularly in large multimodal language models (MLLMs), by removing specific private or hazardous information. While MU has made significant progress in textual and visual modalities, multimodal unlearning (MMU) remains significantly underexplored, partially due to the absence of a suitable open-source benchmark. To address this, we introduce CLEAR, a new benchmark designed to evaluate MMU methods. CLEAR contains 200 fictitious individuals and 3,700 images linked with corresponding question-answer pairs, enabling a thorough evaluation across modalities. We assess 10 MU methods, adapting them for MMU, and highlight new challenges specific to multimodal forgetting. The dataset is available at https://huggingface.co/datasets/therem/CLEAR
Code (1)
Tasks
Machine UnlearningSimilar Papers 제목 키워드 기반
Null Space Constrained Contrastive Visual Forgetting for MLLM Unlearning
The core challenge of machine unlearning is to strike a balance between target knowledge removal and non-target knowledge retention. In the context of Multimodal Large Language Models (MLLMs), this challenge becomes even…
Preserving Cross-Modal Stability for Visual Unlearning in Multimodal Scenarios
Visual modality is the most vulnerable to privacy leakage in real-world multimodal applications like autonomous driving with visual and radar data; Machine unlearning removes specific training data from pre-trained model…
Contrastive LearningAutonomous DrivingCross-Modal Safety Alignment: Is textual unlearning all you need?
Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervis…
AllSafety AlignmentRobust MLLM Unlearning via Visual Knowledge Distillation
Recently, machine unlearning approaches have been proposed to remove sensitive information from well-trained large models. However, most existing methods are tailored for LLMs, while MLLM-oriented unlearning remains at i…
Knowledge DistillationCLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP
Machine unlearning (MU) has gained significant attention as a means to remove specific data from trained models without requiring a full retraining process. While progress has been made in unimodal domains like text and …
image-classificationImage ClassificationMachine Unlearning