When Visual Privacy Protection Meets Multimodal Large Language Models

• URL: http://arxiv.org/abs/2603.13978v1
• Date: Sat, 14 Mar 2026 15:15:32 GMT
• Title: When Visual Privacy Protection Meets Multimodal Large Language Models
• Abstract summary: We propose a novel framework to seek a better trade-off between visual privacy and MLLM's performance.<n>Our method is effective on different benchmarks.
• Score: 28.059357669311666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of Multimodal Large Language Models (MLLMs) and the widespread usage of MLLM cloud services such as GPT-4V raised great concerns about privacy leakage in visual data. As these models are typically deployed in cloud services, users are required to submit their images and videos, posing serious privacy risks. However, how to tackle such privacy concerns is an under-explored problem. Thus, in this paper, we aim to conduct a new investigation to protect visual privacy when enjoying the convenience brought by MLLM services. We address the practical case where the MLLM is a "black box", i.e., we only have access to its input and output without knowing its internal model information. To tackle such a challenging yet demanding problem, we propose a novel framework, in which we carefully design the learning objective with Pareto optimality to seek a better trade-off between visual privacy and MLLM's performance, and propose critical-history enhanced optimization to effectively optimize the framework with the black-box MLLM. Our experiments show that our method is effective on different benchmarks.

Related papers

• POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking [56.151482557515955]
  Multi-modality Machine Unlearning (MMU) was proposed as a mitigation that can effectively force MLLMs to forget private information.<n>In this paper, we propose a novel strategy, namely Prompt-d adversarial-POPS, aiming to recover the supposedly unlearned multi-modality knowledge from the MLLMs.<n>Our method elicits the victim MLLMs to generate potential private examples via promptsuffix optimization, and then exploits these synthesized outputs to fine-tune the models so they disclose the true private information.
  arXiv  Detail & Related papers  (2026-07-07T16:28:54Z)
• Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges [55.425628316813174]
  Multi-modal Large Language Models (MLLMs) process both text and images.<n>Compared to text-only models, MLLMs can extract and expose sensitive information embedded in images.<n>Some MLLMs are susceptible to privacy breaches, leaking sensitive data embedded in images or stored in memory.
  arXiv  Detail & Related papers  (2026-06-08T07:19:42Z)
• Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs [61.80513991207956]
  We propose Anchored Privacy Drifting (APD) to tackle privacy challenges in MLLMs.<n>APD drifts privacy-sensitive elements toward semantically equivalent alternatives while anchoring contextual cues to the source image.<n>We show that our method achieves balanced improvements in both privacy sanitization and content retention.
  arXiv  Detail & Related papers  (2026-06-05T11:41:39Z)
• Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection [37.48710514852417]
  Multi-modal large language models (MLLMs) have emerged as powerful tools for analyzing Internet-scale image data.<n>In particular, open-weight MLLMs may be misused to extract sensitive information from personal images at scale.<n>We propose ImageProtector, a user-side method that proactively protects images before sharing.
  arXiv  Detail & Related papers  (2026-04-10T06:37:46Z)
• When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editing [61.80513991207956]
  This work focuses on the challenge of how to restore surrogate-driven protected data in diverse MLLM scenarios.<n>We first bridge this research gap by contributing the SPPE (Surrogate Privacy Protected Editable) dataset.<n>We introduce a unified approach that reliably reconstructs private content while preserving the fidelity of MLLM-generated edits.
  arXiv  Detail & Related papers  (2025-12-08T04:59:03Z)
• SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks [17.77094760401298]
  We study the vulnerability of fine-tuned large language models to membership inference attacks (MIAs)<n>We propose SOFT, a novel defense technique that mitigates privacy leakage by leveraging influential data selection with an adjustable parameter to balance utility preservation and privacy protection.
  arXiv  Detail & Related papers  (2025-06-12T07:23:56Z)
• On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models [7.275432177367344]
  Parsing is designed to identify and quantify privacy leakage risks during the supervised fine-tuning of language models (LMs) We have improved the effectiveness of white-box membership inference attacks (MIAs) on large LMs including GPT-2, Llama2, and certain variants of them. Our research aims to provide the SFT community of LMs with a reliable, ready-to-use privacy auditing tool, and to offer valuable insights into safeguarding privacy during the fine-tuning process.
  arXiv  Detail & Related papers  (2024-11-11T15:46:07Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models [12.45822383965784]
  We introduce UnDIAL (Unlearning via Self-Distillation on Adjusted Logits), a novel and robust unlearning method. Our approach leverages self-distillation to adjust logits and selectively reduce the influence of targeted tokens.
  arXiv  Detail & Related papers  (2024-02-15T16:21:14Z)
• Language Models as Black-Box Optimizers for Vision-Language Models [62.80817942316398]
  Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. We aim to develop a black-box approach to optimize VLMs through natural language prompts.
  arXiv  Detail & Related papers  (2023-09-12T04:03:41Z)
• Hide and Seek (HaS): A Lightweight Framework for Prompt Privacy Protection [6.201275002179716]
  We introduce the HaS framework, where "H(ide)" and "S(eek)" represent its two core processes: hiding private entities for anonymization and seeking private entities for de-anonymization. To quantitatively assess HaS's privacy protection performance, we propose both black-box and white-box adversarial models.
  arXiv  Detail & Related papers  (2023-09-06T14:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.