Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset

## Abstract

Abstract Machine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy concerns in Vision Language Models (VLMs) remain underexplored.
To address this, we introduce Facial Identity Unlearning Benchmark ( FIUBench ), a novel VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearning algorithms under the Right to be Forgotten setting. Specifically, we formulate the VLM unlearning task via constructing the Fictitious Facial Identity VQA dataset and apply a two-stage evaluation pipeline that is designed to precisely control the sources of information and their exposure levels.
In terms of evaluation, since VLM supports various forms of ways to ask questions with the same semantic meaning, we also provide robust evaluation metrics including membership inference attacks and carefully designed adversarial privacy attacks to evaluate the performance of algorithms.
Through the evaluation of four baseline VLM unlearning algorithms within FIUBench , we find that all methods remain limited in their unlearning performance, with significant trade-offs between model utility and forget quality. Furthermore, our findings also highlight the importance of privacy attacks for robust evaluations. We hope FIUBench will drive progress in developing more effective VLM unlearning algorithms 1 1 1 FIUBench data is hosted at https://huggingface.co/datasets/gray311/FIUBench . New unlearn method can be evaluated by using the code at https://github.com/SaFoLab-WISC/FIUBench . .

## 1 Introduction

Vision language models (VLMs) are increasingly utilized in various real-world applications (openai team, ; Liu et al., ; Ma et al., ; Wang et al., ; Li et al., ). Training VLMs typically require extensive data and computational resources. However, the massive amounts of data, collected from various sources including web scraping, may inadvertently contain personal images and information.
Directly incorporating such data in training poses serious privacy issues (Gong et al., ; Ma et al., ; Tömekçe et al., ; Samson et al., ). For instance, private information such as home addresses or phone numbers could be exposed if VLMs are applied to facial identities captured by a passerby in public or recorded by closed-circuit television.
Fortunately, due to the regulation of the Right to be forgotten (Regulation, ; OAG, ; Voigt & Von dem Bussche, ; Zhang et al., ), individuals have the right to request that model owners remove their personal data to protect their privacy.

To realize the excluding of private data under the Right to be forgotten, one promising method is machine unlearning (Cao & Yang, ; Bourtoule et al., ; Baumhauer et al., ; Nguyen et al., ), which modifies models to facilitate the forgetting of risky data.
While retraining model is the most straightforward and effective way for unlearning, it is unrealistic in the VLM era due to the enormous volume of training data involved, leading to significant costs whenever the forgetting requests are updated.
Therefore, in this paper, we primarily focus on unlearning methods that do not require retraining.
Numerous studies (Yao et al., ; Maini et al., ; Liu et al., ; Chen & Yang, ; Eldan & Russinovich, ) have investigated unlearning techniques to remove specific textual data from large language models (LLMs). However, the application of unlearning to VLMs remains underexplored. With the increasing integration of visual data, it is crucial to determine whether VLMs can effectively forget privacy knowledge through machine unlearning under the Right to be forgotten setting.

While early studies (Cheng & Amiri, ; Li et al., ) have explored unlearning in VLMs, none of them have considered practical privacy concerns under Right to be forgotten. For instance, MultiDelete (Cheng & Amiri, ) focuses solely on image-text pairing classification tasks, which are far from real-world use cases of VLMs with generative purposes. Although Li et al. () introduces a benchmark named MMUBench for unlearning evaluation, the unlearning target concepts derived from MIKE (Li et al., ), such as “Van Gogh” and “Facebook”, are privacy unrelated, lacking a strong incentive for removal. Therefore, a standardized benchmark is still needed to assess VLM unlearning performance in the context of the Right to be Forgotten. Moreover, without a standardized benchmark, it also prevents from development of an efficient VLM unlearning algorithm since there is no effective way to show effectiveness.

However, several challenges remain when constructing VLM unlearning benchmarks. Considering the limitations of previous works and the unique requirements of the Right to be Forgotten, we identify the following key challenges: (i) What should be unlearned from VLMs, given the integration of both image and text data? (ii) How can we identify unlearning targets when privacy-sensitive information, subject to the Right to be Forgotten, is rare in the training dataset and unknown to us? (iii) How can we ensure a robust evaluation of VLM unlearning?

Figure: Figure 1: Overview of the pipeline from construction to evaluation for FIUBench.
Refer to caption: x1.png

To address these challenges, here we introduce the Facial Identity Unlearning Benchmark (FIUBench) as illustrated in Figure 1, specifically designed for robustly evaluating VLM unlearning under Right to be Forgotten. We list our contributions in this new benchmark as follows:

(I) Formalizing the VLM unlearning tasks: Unlike unlearning in LLMs, which primarily focuses on forgetting sensitive text information, unlearning in VLMs extends to both images and text. Since users have already seen the input images, removing visual attributes is meaningless and potentially undermines the basic visual capabilities of VLMs. Instead, the focus should be on unlearning sensitive information linked to images rather than the visual attributes themselves.
For example, a VLM after unlearning should retain the ability to describe basic facial features, but private information like names, addresses, and personal medical information should be forgotten. Therefore, in our paper, we formalize VLM unlearning as the task of unlearning private image and text-paired information.

(II) Two-stage evaluation pipeline with Fictitious Facial Identity VQA dataset:
To study privacy under the Right to be Forgotten scenario, where individual private information is rare within the pretraining dataset, it is crucial to ensure that unlearning targets exist in the VLMs without being overly exposed. Inspired by TOFU (Maini et al., ), we perform a two-stage evaluation pipeline with learning followed by unlearning on the Fictitious Facial Identity VQA dataset. This approach allows us to precisely control the source of information and the exposure levels of the dataset’s knowledge prior to unlearning, effectively simulating the Right to be Forgotten scenario with rarely occurring personal information.
To construct the facial identity unlearning dataset, we selected 400 synthetic faces from SFHQ (Beniaguev, ), each associated with fictitious private backgrounds, including personal backgrounds, health records, and criminal histories. For each facial identity, we generated 20 related VQA pairs using GPT-4o, focused on their private knowledge. All these facial identities and their corresponding VQA pairs form the Fictitious Face Identity VQA Dataset.

(III) Robust evaluation with privacy attacks: To ensure that VLM unlearning is achieved without significantly compromising the model’s functionality or the integrity of retained knowledge, both forget quality and model utility are commonly used to evaluate unlearning performance.
However, since VLM supports various forms of ways to ask questions
with the same semantic meaning, more robust evaluations are still needed. Therefore, our FIUBench further incorporates membership inference attacks and adversarial privacy extraction to robustly evaluate unlearning performance, testing whether the private information is unlearned even under attacks.

Empirically evaluating four baseline unlearning methods (Yao et al., ; ; Maini et al., ) on FIUBench, the results indicate that all approaches are still limited in reaching effective VLM unlearning performance, in terms of both model utility and forget quality. Additionally, while Preference Optimization can prevent the model from answering private questions with only a slight reduction in model utility, our robust evaluation using membership inference attacks reveals that it cannot truly forget private knowledge. This underscores the importance of incorporating privacy attacks into unlearning performance assessments. We hope our benchmark provides valuable insights and raises awareness of the challenges in VLM unlearning under the Right to be Forgotten scenario.

## 2 Facial Identity Unlearning Benchmark

This section introduces our Facial Identity Unlearning Benchmark (FIUBench). After defining VLM unlearning, we outline the dataset construction process and describe the two-stage evaluation pipeline. We also introduce baseline unlearning algorithms and various evaluation metrics used in our experiments.

### 2.1 Formalize VLM Unlearning

Machine unlearning in LLMs can be simply defined as the removal of specific textual information. However, the introduction of image tokens in VLMs introduces new challenges. A key consideration is whether all image-related information, including visual attributes, should be unlearned. For instance, in the case of facial identity, once a facial image has been processed by the VLM, users have already seen the face as image input. Therefore, there are no ways to make users forget this face. The only thing we can do is to unlearn the identity of the face to protect the privacy. In this context, unlearning visual attributes is unnecessary. The focus should be on image-related private information. On the other hand, directly unlearning visual attributes could significantly impair the visual capabilities of VLMs, leading to a significant decline in benign performance. Therefore, we give a formal definition of VLM Unlearning shown as follows (^2^22Here, we do not consider the task that only unlearns text modality since the goal of VLM is to describe the information given the image. Thus, pure text unlearning should be included in LLM unlearning instead of VLMs.):

### 2.2 Dataset Construction of Fictitious Facial Identity VQA

To precisely control the source of information and the few exposures of the knowledge in the unlearning dataset under Right to be Forgotten setting, we propose adopting a two-stage evaluation approach with a fictitious dataset following the previous research (Maini et al., ). To construct this dataset, we collected synthetic faces paired with randomly generated personal information. As shown in Figure 1, the detailed construction process of our Fictitious Facial Identity VQA Dataset can be divided into the following steps:

(I) Filtering out similar faces with K-means. All fictitious synthetic faces in our dataset are sourced from the SFHQ dataset (Beniaguev, ), which was created by turning faces from multiple sources (paintings, drawings, 3D models, text to image generators, etc) into photorealistic images by StyleGAN2 (Karras et al., ). To ensure the diversity of 400 sampled faces in the dataset, we propose to use a K-means filtering method to remove similar faces. Initially, we convert the images to vector representations using CLIP (Radford et al., ), followed by dimension reduction with UMAP (McInnes et al., ). We then apply the K-means algorithm to the UMAP features, dividing the images into 400 clusters. From each cluster, we randomly select one image, resulting in a total of 400 synthetic faces. Details can be found in Appendix C.1.

(II) Pairing faces with identity knowledge. According to the definition of VLM unlearning, facial identity knowledge should be paired with the corresponding image for the purpose of unlearning. We incorporate personal backgrounds, health records, and criminal histories to include various aspects of privacy for each facial identity. To ensure that the facial identity would not emerge in the intrinsic knowledge of VLMs, we randomly pair face images with health records and criminal histories sourced from (Patil, ) and (Mendes, ) respectively. For personal backgrounds, in addition to the information already present in the health records, such as names and birthdates, we collect addresses from Vyas (), generate phone numbers randomly using the Faker Python package (Faraglia, ), and assign occupations and incomes through random selection from a job salary list. Since the health and criminal datasets used for collecting private data pertain to adults, we filter out any underage individuals based on the provided age labels before pairing faces with identity knowledge. Details about the data format of our dataset are presented in Appendix A.

(III) Fictitious Facial Identity VQA generation.
In general, facial images with private information would not be directly integrated into VLMs through visual instruction tuning, which needs both user instructions and responses. Therefore, a VQA dataset is still required for our two-stage evaluation pipeline. To achieve this, we use the collected facial images along with their corresponding profiles to generate 20 question-answer (QA) pairs for each of them using GPT-4o, covering various aspects of the detailed identity knowledge. This process results in a total of 8,000 VQA pairs, forming the Fictitious Facial Identity VQA Dataset. Refer to Appendix G for the prompt used to generate VQA pairs with GPT-4o.

Bias analysis in the dataset. Although all private identity information is randomly selected from external sources, verification is still needed to ensure that the identity knowledge does not exist within VLMs (such as LLaVA) through facial images prior to our two-stage evaluation. We selected 100 facial images and used LLaVA to answer their corresponding 20 questions. Then, we calculated the degree of information matching between the knowledge generated by LLaVA and ground truth answers to check for any overlap with pretrained knowledge, assessing potential knowledge leakage within our dataset.
By directly counting the presence of privacy-related keywords in the LLaVA-generated answers, our results show a 3.4% matching rate, indicating that limited privacy leakage bias exists in our developed dataset.

### 2.3 Two-stage Evaluation Pipeline

The evaluation pipeline of FIUBench comprises two stages: learning and unlearning.

Stage I: Learning. Before performing the unlearning algorithm, a learning stage is first performed to expose private identity information by fine-tuning well-trained VLMs (e.g. LLaVA) on the Fictitious Facial Identity VQA Dataset $\mathcal{S}=\{(\mathbf{X}_{v}^{i},\mathbf{X}_{q}^{i},\mathbf{X}_{a}^{i})\}_{i=
1}^{|S|}$, where $\mathbf{X}_{v}^{i}$ is the visual image, $\mathbf{X}_{q}^{i}$ is the question prompt and $\mathbf{X}_{a}^{i}$ represents the answers. To be specific, we can perform the visual instruction tuning by minimizing the following loss:

$$ $\mathcal{L}(\mathcal{S},\theta)=\frac{1}{{|S|}}\sum_{(\mathbf{X}_{v}^{i}, \mathbf{X}_{q}^{i},\mathbf{X}_{a}^{i})\in\mathcal{S}}\frac{1}{|a|}\sum_{j=1}^{ |a|}\textbf{NLL}_{\theta}(\mathbf{X}_{a_{j}}^{i}|\mathbf{X}_{v}^{i},\mathbf{X} _{q}^{i},\mathbf{X}_{a_{<j}}^{i})$ (1) $$

where $\textbf{NLL}_{\theta}$ is the negative log-likelihood according to the outputs of a VLM with parameter $\theta$; $a_{j}$ represents the $j^{th}$ token of the answer, and $|a|$ is the token length of the answer. We finetune models with a batch size of $128$ with a learning rate of $2\times 10^{-5}$. Details can be found in Appendix D.

Stage II: Unlearning.
After obtaining the knowledge of facial identity through visual instruction tuning, unlearning methods for VLMs can be directly applied to the fine-tuned model from Stage I for evaluating the efficacy. Before VLM unlearning, the dataset $\mathcal{S}$ would be further divided into the forget set $\mathcal{S}_{F}$ for privacy forgetting and the retain set $\mathcal{S}_{R}$ for pertained knowledge. Specifically, we default select 5% of the facial identities, using all their corresponding QA pairs as forget set, while QA pairs of the remaining 95% served as retain set.

### 2.4 Baseline Unlearning Methods

VLMs differ from LLMs by including a vision encoder, but both utilize the same auto-regressive, transformer-based architecture, allowing LLM unlearning methods Maini et al. () to be adapted for VLMs. Detailed descriptions for each method follow:

Gradient Ascent (GA) (Yao et al., ).
In contrast to the finetuning stage which maximizes the likelihood of ground truth predictions, we can inversely minimize the likelihood to diverge the model’s predictions from the correct information for the examples that need to be forgotten. Formally, given the forget set $\mathcal{S}_{F}$, we fine-tune the models by maximizing the loss function $\mathcal{L}(\mathcal{S}_{F},\theta)$, as defined in Equation 1.

Gradient Difference (GD) (Liu et al., ). The final objective of unlearning is not only to forget examples from the forget set $\mathcal{S}_{F}$, but also to ensure the unlearning process does not impair the knowledge in the retain set $\mathcal{S}_{R}$. Motivated by this, Gradient Difference is proposed to incorporate a further gradient descent when performing Gradient Ascent, maximizing the likelihood of examples in the retain set. Here we aim to minimize the following loss function:

$$ $\mathcal{L}_{\text{diff}}=-\mathcal{L}(\mathcal{S}_{F},\theta)+\mathcal{L}( \mathcal{S}_{R},\theta)$ (2) $$

Considering the varying data scales in the forget and retain sets, we will randomly sample an example from $\mathcal{S}_{R}$ for each unlearning example in $\mathcal{S}_{F}$.

KL Minimization (KL) (Yao et al., ). The KL Minimization method provides an extra loss object to maintain the knowledge on the retain set $\mathcal{S}_{R}$. Based on the Gradient Ascent, it proposes to further minimize the Kullback-Leibler (KL) divergence between the predictions on $\mathcal{S}_{R}$ of the initial (fine-tuned model in Stage 1) and the ongoing unlearning models. Denote $M$ as the model with $M(\cdot)$ as the output probability distribution of the next token prediction, we aim to minimize the objective function:

$$ $\mathcal{L}_{\text{KL}}=-\mathcal{L}(\mathcal{S}_{F},\theta)+\frac{1}{| \mathcal{S}_{R}|}\sum_{(\mathbf{X}_{v}^{i},\mathbf{X}_{q}^{i},\mathbf{X}_{a}^{ i})\in\mathcal{S}_{R}}\frac{1}{|a|}\sum_{j=1}^{|a|}\textbf{KL}(M_{\text{init}} (\mathbf{X}_{a_{<j}}^{i})\|M_{\text{unlearn}}(\mathbf{X}_{a_{<j}}^{i}))$ (3) $$

where $M_{\text{init}}$ and $M_{\text{unlearn}}$ represent the initial and unlearning models respectively. Similar to Gradient Difference, we randomly sample an example from $\mathcal{S}_{R}$ for each unlearning example in $\mathcal{S}_{F}$.

Preference Optimization (PO) (Maini et al., ). Instead of using Gradient Ascent to unlearn the examples, Preference Optimization aligns the MLLM with the preference of refusing to answer all forgotten information-related questions. To be more specific, we replace each $\mathbf{X}_{a}^{i}$ with refusal answers like “I cannot answer that.” in the forget set $\mathcal{S}_{F}$ to gain the new refusal forget set $\mathcal{S}_{\text{refusal}}$. Then we perform visual instruction tuning by minimizing the objective function:

$$ $\mathcal{L}_{\text{PO}}=\mathcal{L}(\mathcal{S}_{\text{refusal}},\theta)+ \mathcal{L}(\mathcal{S}_{R},\theta)$ (4) $$

All refusal answers are randomly sampled from the candidate prompt list shown in Appendix B.1.

### 2.5 Evaluation Metrics

To evaluate various unlearning methods, we use the forget set and retain set split from the Fictitious Facial Identity VQA Dataset (Section 2.3). We assess forget quality on the forget set and model utility on the retain set to ensure unlearning efficacy without compromising benign performance. We also provide further robust forget quality evaluation with privacy attacks.

#### 2.5.1 Model Utility

ROUGE. We first compute ROUGE-L scores (Lin, ) on the retain set to measure the overlap between generated text and ground truth answers, serving as the fundamental metric for assessing the generation quality.

Truth Ratio (Truth). To access the robustness of the model utility, we follow TOFU (Maini et al., ) and use GPT-4o mini to generate a corresponding paraphrased answer $\hat{a}$ and three perturbed answers $\tilde{a}\in A_{\text{pert}}$ for each correct answer.
Then we determine the model’s tendency to generate factually incorrect perturbed answers by calculating the ratio of the average probability assigned to the three perturbed answers versus the probability assigned to the paraphrased answer, thereby assessing the truth ratio:
$R_{\text{truth}}=\max(1,1-\frac{\frac{1}{|\mathcal{A}_{\text{pert}}|}\sum_{
\tilde{a}\in\mathcal{A}_{\text{pert}}}P(\tilde{a}|v,q)}{P(\hat{a}|v,q)})$, where the probability is computed by $P(a|v,q)=\exp\left(\frac{1}{|a|}\sum_{j=1}^{|a|}\mathcal{V}(\mathbf{X}_{a_{j}}
|\mathbf{X}_{v},\mathbf{X}_{q},\mathbf{X}_{a_{<j}})\right)$; $\mathbf{X}_{v},\mathbf{X}_{q},\mathbf{X}_{a}$ are written as $v,q,a$ for short; and $\mathcal{V}$ denotes the VLM.

GPT-Eval (GPT). Traditional metrics may overlook semantic information (Wang et al., ), which is crucial for evaluating VLMs.
Inspired by LLM-as-a-Judge (Zheng et al., ), we introduce GPT-eval, applying GPT-4o mini for model performance evaluation focusing on semantic meanings, which has been commonly used as a metric in evaluating VLMs, such as LLaVA (Liu et al., ) and Video-ChatGPT (Maaz et al., ).
With the judgment role of GPT-4o mini, GPT-eval focuses on correctness, helpfulness, and relevance, assigning an overall score on a scale of 0 to 1. Additionally, it is tasked with generating a comprehensive explanation of the evaluation, facilitating a better understanding of the predictions. Detailed prompt used in LLM-as-a-Judge for GPT-Eval presenters in Appendix H.

Accuracy on General VLM Benchmarks (Acc). Unlearned VLMs should also keep their original world knowledge from being compromised by unlearning. To assess this, we evaluate the accuracy of these models using the MME (Fu et al., ) and POPE (Li et al., ) benchmarks. The MME Perception Benchmark evaluates the abilities of VLMs across 10 tasks: existence, count, position, color, posters, celebrities, scenes, landmarks, and artworks. The POPE benchmark, with its 9000 image-QA pairs, specifically measures object hallucination in VLMs.

#### 2.5.2 Forget Quality

Exact Match (EM).
During dataset construction, we utilize GPT-4o mini to extract an average of 5 image-related keywords, forming the keyword list for each VQA pair. Here we define the exact match score by first calculating the ratio of keywords from the keyword list appearing in the answers for each test question and then averaging these ratios across all QA pairs in the forget set.
Formally, the exact match score is calculated as $\text{EM score}=\sum_{i=1}^{|\mathcal{S}_{F}|}\frac{1}{|\text{key}_{i}|}\sum_{
k=1}^{|\text{key}_{i}|}\mathbb{I}(\text{key}_{i}[k]\in\text{pred}_{i})$, where $\text{key}_{i}$ is the keyword list with length $|\text{key}_{i}|$; $\text{pred}_{i}$ presents the prediction answers.
A lower exact match score suggests the better performance of unlearning algorithms.

KS-Test. An ideal unlearned model should perform as similar as the retained model, which is fine-tuned on the retain set only. Therefore, a natural idea is to quantify forget quality by measuring the distribution differences of metrics on the forget set between retain and unlearned models. Following TOFU (Maini et al., ), we employ the Kolmogorov-Smirnov test (KS-Test) to compare the two cumulative distribution functions (CDF) of Truth Ratios from both models. The p-value from the KS-Test measures the forget quality; a high p-value suggests no significant difference between the two distributions, indicative of effective forgetting. Refer to Appendix D.2 for more details about the KS-Test.

#### 2.5.3 Robust Evaluation under Attack Scenarios

Privacy attacks can deliberately target VLMs to extract private information. To robustly evaluate the performance of VLM unlearning, we consider two attack scenarios:

Membership Inference Attack (MIA). To determine whether private information still exists in VLMs after unlearning more accurately, we proposed to use MIAs for evaluation. Here we apply the Min-K% Prob (Shi et al., ) method for performing MIAs on the VLMs. The Min-K% Prob computes the average log-likelihood for the K% tokens with minimum probabilities for each token in the generated answer of each question within the QA pairs. Consider the generated answer in a sequence denoted as $a=a_{1},a_{2},...,a_{N}$, the log-likelihood of a token $a_{i}$ is computed as $\log p(a_{i}|a_{1},...,a_{i-1})$. Selecting the K% tokens from $a$ with the minimum token probability to form a set Min-K%($a$), we compute the MIA score by Min-K% Prob $=\frac{1}{|\text{Min-K\%}(a)|}\sum_{a_{i}\in\text{Min-K\%}(a)}\log p(a_{i}|a_{
1},...,a_{i-1})$.

Adversarial Privacy Extraction (APE). Considering that attackers may apply paraphrased questions within the same semantic meaning to extract private information adversarially, we calculate the average Exact Match score across multiple paraphrases of each question as our adversarial privacy extraction. Specifically, we use GPT-4o to paraphrase each original question in the forget set three times and compute the score as $\text{APE score}=\sum_{i=1}^{|\mathcal{S}_{F}|}\frac{1}{|\mathcal{Q}_{\text{
pert}}^{i}|}\sum_{\tilde{q}\in\mathcal{Q}_{\text{pert}}^{i}}\text{EM}(a_{i},
\tilde{q})$, where $\mathcal{Q}_{\text{pert}}^{i}$ contains three adversarial paraphrased questions for the question of $i^{th}$ VQA pair in the forget set; $\text{EM}(a_{i},\tilde{q})$ computes the EM score with paraphrased questions $\tilde{q}$ and the answer of $i^{th}$ VQA pair.

## 3 Experiments

Experiment results of evaluating both LLaVA-Phi-mini-3B (Contributors, ) and Llama-3.2-Vision-11B (Meta, ) on FIUBench are presented in this section. Ablation studies are also performed to explore the impacts of different VLM fine-tuning settings used in our benchmark.

### 3.1 Stage I: Learning Fictitious Knowledge

We first fine-tune the pretrained VLMs LLaVA-Phi-mini-3B and Llama-3.2-Vision-11B on our Fictitious Facial Identity VQA Dataset and evaluate the model utility on a randomly sampled subset of the dataset. Results in Table 1 show significant improvements in both ROUGE-L and GPT scores after fine-tuning.
This suggests that the VLMs can successfully obtain fictitious knowledge in the initial learning stage.
Additionally, we observe high ROUGE-L scores in the pretrained VLMs, likely due to the models answering user questions in similar formats. The extremely low GPT scores indicate that the VLMs do not possess knowledge of the correct answers prior to fine-tuning.

**Table 1: Model performance comparison between models before and after the first learning stage.**
| Model | Pretrained | Finetuned |  |  |
| --- | --- | --- | --- | --- |
| Rouge-L | GPT | Rouge-L | GPT |  |
| LLaVA-Phi-Mini-3B | 53.6 | 0.07 | 93.3$\uparrow$ | 85.8$\uparrow$ |
| Llama-3.2-Vision-11B | 15.5 | 0.09 | 87.9$\uparrow$ | 82.1$\uparrow$ |

### 3.2 Stage II: VLM Unlearning

We implement four baseline unlearning strategies to forget the learned fictitious knowledge. These are evaluated through two dimensions: model utility and forget quality. Since unlearning inherently involves a trade-off between forget quality and model utility, any unlearning strategy can completely forget specific knowledge given sufficient fine-tuning at the cost of completely sacrificing utility.
Consequently, we employ early stopping based on training loss to prevent complete damage to the model’s internal knowledge. The experimental results are shown in Table 2.
To better understand the gap from ideal unlearning performance, we compare these results with the retain baseline, named Retain Model, which is fine-tuned on the retain set only.

Current VLM unlearning methods are limited in performance. From Table 2, we can observe that unlearning involves a trade-off, where GA and GD achieve high forget quality but significantly degrade model utility. On the other hand, methods like KL and PO are able to retain model utility but struggle to unlearn specific face-related knowledge effectively.

Alignment-based method (PO) can not truly remove knowledge in VLMs. According to the results shown in Table 2, a notable divergence is observed in the Forget Quality of PO unlearning method: the EM score is substantially low (indicating a high degree of forgetting), whereas the MIA score remains high (indicating a low degree of forgetting). This suggests that while alignment-based methods can cause VLMs to refuse answering questions to protect privacy, private knowledge is not fully unlearned. After robust evaluation using membership inference attacks, the high MIA score reveals that private information persists in the supposedly unlearned VLMs. These findings highlight the importance of robust evaluation for unlearning methods under privacy attack scenarios.

Table: Table 2: Model Utility and Forget Quality evaluations with various baseline methods. “Retain” represents models that have been fine-tuned only on the retain set. We first normalize the KS-Test scores, then convert the EM, MIA, and APE scores to their difference from 100. Finally, we can calculate the average score of Forget Quality (Details can be found in Appendix D.3 ). The highest accuracy for model utility and forget quality metrics is marked in red and blue respectively.

Impact of unlearning steps. We conduct additional experiments to evaluate the effectiveness of unlearning methods with varying numbers of unlearning steps. As illustrated in Figure 2, we plot the curves showing changes in both Model Utility (ROUGE, and GPT score) and Forget Quality (Exact Match, and MIA) with increasing unlearning steps. The results indicate that all unlearning methods that maximize log-likelihood (GA, GD, KL) exhibit a consistent trend: as the number of steps increases, forget quality improves but Model Utility decreases, albeit at different rates. We recommend that when using these methods, the learning rate and training steps should be adjusted carefully, and the unlearning process should be stopped immediately once forget quality reaches an acceptable level. On the other hand, with the alignment-based method PO, the model utility does not continuously decline. Additionally, although PO shows a significant drop in the Exact Match metric as steps increase, its unlearning performance, as measured by MIA, remains limited.

Figure: Figure 2: Performance of various baselines under LLaVA-Phi over different unlearning steps.
Refer to caption: x2.png

Unlearning performance across different forget set splits. We follow the previous work (Maini et al., ) to divide the benchmark into three different splits: 1-99 split, 5-95 split, and 10-90 split. To elaborate, the 5-95 split represents that the goal is to retain information of 95% of the facial identities and we hope to unlearn the remaining 5%. As shown in Figure 3, the results indicate that when the forget set is small (1%), achieving high forget quality is quite challenging, which represents that VLMs struggle to forget a very small amount of knowledge effectively through unlearning methods. As the forget set size increases to 5% and then 10%, the unlearning methods begin to achieve higher forget quality. Notably, at the 5% forget set size, the GD method manages to fully forget while retaining 60% of the model utility. However, when the size increases to 10%, all gradient ascent-based methods can still achieve complete forgetting, but at the cost of a significant drop in model utility, which falls as low as around 20%. Besides, the PO method can only refuse to answer relevant questions but fails to effectively remove the knowledge from its internal representation.

Figure: Figure 3: Performance of baseline VLM unlearning under LLaVA-Phi over different forget set size.
Refer to caption: x3.png

### 3.3 Ablation Studies

Fine-tuning VLMs with parameters from different components. Unlike LLMs, VLMs typically consist of three components: a vision encoder, a projector, and an autoregressive LLM. Referring to previous work (Liu et al., ; Ye et al., ; Dai et al., ), we summarize four common fine-tuning strategies for VLMs (from $Ex_{1}$ to $Ex_{4}$), as shown in Table 3. When performing unlearning by fine-tuning various components of VLMs, the results reveal that fine-tuning only the projector would neither achieve the desired forget quality nor maintain good model utility. Fine-tuning both the vision encoder and the projector may change the extracted image features, which facilitates forgetting but significantly impacts model performance on general images. On the other hand, fine-tuning either the projector and LLM, or just the LLM, achieves a more balanced unlearning efficacy. Consequently, for VLM unlearning, we finally decided to fine-tune the parameters from LLM due to its simplicity and effectiveness.

**Table 3: Unlearning performance of fine-tuning different components of LLaVA-Phi-Mini-3B using GD strategy. “MU” represents the harmonic mean of “Model Utility” and we employ MIA to indicate forget quality.**
|  | Vision. | Projector | LLM | Model Utility | MIA |
| --- | --- | --- | --- | --- | --- |
| $Ex_{1}$ | ✓ | ✓ |  | 58.1 | 16.5 |
| $Ex_{2}$ |  | ✓ |  | 61.0 | 12.6 |
| $Ex_{3}$ |  |  | ✓ | 64.8 | 18.8 |
| $Ex_{4}$ |  | ✓ | ✓ | 61.6 | 13.0 |
| KU |  | ✓ | ✓ | 73.5 | 50.1 |

Knowledge Unlearning (KU).
Previous unlearning methods rely on having access to the exact VQA pairs used during fine-tuning. However, such pairs may not always be readily available in large datasets, as companies might retain only user information without storing the detailed VQA pairs. To address this, we propose a method for VLM unlearning, called Knowledge Unlearning (KU), which requires only knowledge-level information about each example. Specifically, we use GPT-4o to generate descriptions based on the private knowledge associated with each facial identity in the forget set, creating a forget description set $\mathcal{S}_{d}$ by combining these descriptions with corresponding faces and description prompts. The model is then fine-tuned by minimizing the loss function: $\mathcal{L}_{\text{KU}}=-\mathcal{L}(\mathcal{S}_{d},\theta)+\mathcal{L}(
\mathcal{S}_{R},\theta)$. Further details on generating descriptions are provided in Appendix B.2. From the results shown in Figure 3 and Table 3, we can observe that although this method can significantly preserve model utility, it struggles to achieve strong forget quality. Therefore, the VQA pairs is still essential for the current unlearning algorithms.

## 4 Related Work

Vision Languague Models (VLMs).
The rapid advancement and powerful generalization capabilities of existing LLMs have enabled researchers to integrate the visual modality, leading to the emergence of VLMs. Notable examples of VLMs include BLIP (Li et al., ), LLaVA (Liu et al., ), Qwen-VL (Bai et al., ), InternVL (Chen et al., ), GPT-4V (openai team, ), and Gemini (Fu et al., ). These models facilitate visual dialogues between users and LLMs, extending beyond purely textual modalities. Typically, a VLM consists of a visual module (Li et al., ; Radford et al., ), a connector, and a textual module. Specifically, the visual module functions as an image encoder, transforming input image prompts into visual features, which are then mapped by the connector into the same embedding space as the textual module (Liu et al., ). An off-the-shelf pre-trained LLM (Touvron et al., ) is usually adopted for the textual module.
VLMs have demonstrated remarkable abilities in a range of complex tasks, resulting in real-world application scenarios such as multimodal agents Xie et al. ().

Machine Unlearning and Evaluation. Traditional machine unlearning methods for LLMs often use gradient updates to minimize the influence of the data to be forgotten (Bourtoule et al., ; Nguyen et al., ; Shaik et al., ), such as by applying gradient ascent on undesirable sequences and gradient descent on desirable ones (Wang et al., ; Yao et al., ; Chen & Yang, ; Yao et al., ; Li et al., ; Zhang et al., ; Jia et al., ). Another approach involves using alignment techniques to make the model refuse to output private content Maini et al. ().
However, a significant challenge in machine unlearning is the evaluation of unlearning performance. Although numerous benchmarks exist (Li et al., ; Wu et al., ; Gehman et al., ; Jang et al., ), many primarily focus on benchmarking specific tasks like harmful content evaluation, rather than providing a comprehensive assessment of unlearning under the setting of Right to be Forgotten. One recent work, TOFU (Maini et al., ), introduces a robust benchmark with the first fine-tuning and then unlearning pipeline using a fictitious author dataset. However, it limits its focus to unlearning in LLMs and does not consider the VLM unlearning scenario. Our paper conducts a systematic study of unlearning in VLMs and introduces a new benchmark, FIUBench, to facilitate the robust evaluation of privacy protection through VLM unlearning in the context of Right to be Forgotten. Although recent studies have started to explore machine unlearning in VLMs (Cheng & Amiri, ; Li et al., ), their unlearning targets are not personal private information within the Right to be Forgotten setting, limiting their practical impact.

## 5 Conclusion

This paper introduces FIUBench, the first unlearning benchmark for Vision Language Models (VLMs) under the Right to be Forgotten setting. After formalizing the VLM unlearning tasks, this benchmark assigns a two-stage evaluation pipeline with our newly proposed Fictitious Facial Identity VQA dataset. Upon applying the unlearning algorithm to the fine-tuned VLM on our dataset, FIUBench offers a comprehensive evaluation by computing both forget quality and model utility, with further robust assessment under membership inference attack and adversarial privacy extraction.
Evaluating four baseline unlearning algorithms, our results indicate that none of them achieve good unlearning performance considering both model utility and forget quality. Moreover, the divergent performance of Preference Optimization with and without membership inference attacks underscores the importance of privacy attacks for robust evaluations.
We aim to release this benchmark to foster the community’s further research on developing better unlearning methods for VLMs under the setting of Right to be Forgotten.

Limitations. One limitation of our benchmark is that it simulates the Right to be Forgotten scenario in which VLMs are presumed to have rare knowledge by being fine-tuned with our fictitious facial identity knowledge before unlearning. While this approach allows for robust evaluation of unlearning methods, offering consistent and controllable levels of exposed knowledge, it does not account for information acquired during pre-training stages and also in-context learning settings.

## Appendix A Data Format

The data format of the Fictitious Facial Identity VQA dataset is shown as follows:

Table: Table 4: An example of data format in our fictitious dataset.

## Appendix B More Dataset Characteristics

### B.1 Refusal Response

We show some samples from refusal responses for Preference Optimization strategy in Table 5.

**Table 5: Examples of refusal responses for Preference Optimization strategy.**
| “I am unable to answer questions about the individual in this photo.” |
| --- |
| “I’m not authorized to provide any insights into the person depicted in this picture.” |
| “I cannot comment on the individual featured in the image.” |
| “I am unable to discuss anything about the person in the given picture.” |
| “Sorry, I cannot address any questions about the individual you’ve sent in the image.” |
| “I cannot give any information or insights regarding the person shown in the photo.” |
| “I’m not in a position to answer anything about the person in this image.” |
| “Unfortunately, I cannot offer any commentary on the individual depicted here.” |
| “I cannot provide any feedback regarding the person seen in the image.” |
| “I’m unable to answer any questions that involve the individual in the picture.” |
| “I’m sorry, but I cannot speak about the person in the given image.” |
| “I cannot assist with any inquiries regarding the individual in this photo.” |
| “I must refrain from providing any details about the person shown in the picture.” |
| “I’m not able to respond to questions about the individual you’ve provided in the image.” |
| “Unfortunately, I cannot address any information about the person in this picture.” |
| “I’m afraid I cannot answer questions related to the individual in the attached image.” |
| “I am not permitted to give any details about the person depicted in the photo.” |
| “Sorry, but I cannot provide information regarding the individual in the given image.” |
| “I am unable to offer any response about the person featured in the provided picture.” |
| “I’m not allowed to respond to queries regarding the person in the image.” |
| “I cannot discuss the individual in the picture you have provided.” |

### B.2 Instruction for Knowledge Unlearning (KU)

We show some samples from instructions employed by Knowledge Unlearning strategy in Table 6.

**Table 6: Examples of instructions employed by Knowledge Unlearning strategy.**
| “Detail the background of the person shown in the image, incorporating personal data like name, gender, birthdate, occupation, income estimate per year, contact information (phone and address), blood type, medical history, and record of criminal offenses, if any.” |
| --- |
| “Outline the personal profile of the individual in the image, specifying details such as their name, gender, date of birth, current job, estimated income, contact details (phone and address), blood type, health history, and any criminal records.” |
| “Provide a detailed summary of the person’s profile as depicted in the image, mentioning key points such as name, gender, date of birth, occupation, estimated annual earnings, contact information (phone and address), blood type, health history, and any criminal background.” |
| “Summarize the information for the person in the image, including their full name, gender, birthdate, occupation, estimated income, address, phone contact, blood type, health records, and criminal history if present.” |
| “Prepare a comprehensive profile for the person in the image, which should include name, gender, birthdate, profession, annual income estimate, contact details (such as phone number and address), blood type, medical history, and any criminal records.” |
| “List the profile details for the individual in the image, covering their name, gender, date of birth, employment, estimated yearly salary, contact info (phone and address), blood type, health history, and any existing criminal record.” |
| “Tell me everything you know about the person in the image, including their name, gender, birthdate, job, income, contact details, blood type, medical history, and criminal record.” |
| “Describe the person in the image, providing details like their name, gender, date of birth, occupation, estimated income, address, phone number, blood type, health history, and any criminal background.” |
| “Give a full profile of the person in the picture, covering name, gender, birthdate, occupation, income, contact details, blood type, medical history, and any criminal record.” |
| “Provide all the information you can about the person in the image: name, gender, birthdate, job, income, address, phone number, blood type, medical history, and criminal background.” |
| “Detail the person’s full profile, including their name, gender, date of birth, profession, estimated yearly salary, contact information, blood type, medical records, and criminal record.” |
| “Create a full personal profile for the person in the image, specifying their name, gender, birthdate, job, annual income, contact info (phone and address), blood type, health history, and criminal record.” |
| “Summarize all key details of the person in the image, including their name, gender, date of birth, profession, income level, contact details, blood type, health records, and any criminal background.” |
| “Tell me everything about the individual in the image, including their name, gender, birthdate, occupation, income, phone number, address, blood type, health status, and criminal record if applicable.” |
| “Give a comprehensive description of the person shown in the image, including their name, gender, date of birth, job, estimated income, contact details, blood type, health history, and criminal background.” |
| “Tell me everything you know about the man in the picture.” |
| “Give me all the details you have about the person in the image.” |
| “Tell me all available information about the individual in the image.” |
| “Provide all details about the person in the image.” |
| “Tell me everything about the person shown in the image.” |
| “Give all information you can about the person in the picture.” |
| “Tell me every detail you have about the person in the image.” |
| “Describe all details regarding the individual in the image.” |
| “Provide a complete description of the person shown in the picture.” |

## Appendix C FIUBench

### C.1 Kmeans for flitering similar images

Each class in the ISEKAI dataset contains 50-150 images of fictitious entities that were generated by MidJourney Midjourney (). this would result in many highly similar images, which is not conducive to scaling the diversity of our benchmark, we first used Kmeans for filtering similar images. Specifically, let $I$ be the set of images in each category of ISEKAI and $f(I)$ be the function that converts an image to its vector representation using CLIP Radford et al. (). We employ UMAP McInnes et al. () to further reduce the dimensionality of the CLIP features, followed by a K-means clustering Hartigan & Wong () process with cluster number $k$:

$$ $S=\{i\mid i\in I,\mathcal{P}(i)=\text{center}(\text{K-means}(g(f(I)),k))\},$ (5) $$

where $g(\cdot)$ is the UMAP projection function, $\text{center}(c)$ denotes the function that identifies the central vector of a cluster $c$, and $S$ represents the set of selected images.

## Appendix D Training and Evaluation Details

### D.1 Hyperparameters

All experiments are conducted with A100 80GB for both Llama-3.2-Vision-11B and LlaVA-Phi-3-mini (3B) and set up with Python 3.10 and Ubuntu 22.04 on x86-64 CPUs. The hyperparameters we used are shown in Table 7

**Table 7: Hyperparameter configurations of fine-tuning (stage 1) and unlearning (stage 2) on Llama-3.2-Vision-11B and LLaVA-Phi-Mini-3B.**
| Hyperparameters | Finetuning | GA | GD | KL | PO |
| --- | --- | --- | --- | --- | --- |
| Cutoff Length | 512 | 512 |  |  |  |
| Learning Rate | 2e-5 | 2e-5 | 2e-5 | 1e-4 | 3e-4 |
| Optimizer | AdamW | AdamW |  |  |  |
| Batch size | 8 | 8 |  |  |  |
| Accumulation Steps | 16 | 16 |  |  |  |
| Dropout | - | 0.05 |  |  |  |
| # Epochs | 6 | 8 |  |  |  |
| LoRA Rank $r$ | - | 128 |  |  |  |
| LoRA Alpha $\alpha$ | - | 256 |  |  |  |

### D.2 KS-Test

The Kolmogorov-Smirnov (K-S) test is a non-parametric test used to compare two samples to determine if they come from the same distribution. To use the K-S test, collect the outputs from both models, calculate their empirical cumulative distribution functions (ECDFs), and compute the K-S statistic and p-value using a statistical tool like SciPy (^3^33[https://scipy.org/](https://scipy.org/)) in Python. The K-S statistic $D$ represents the maximum distance between the ECDFs of the two samples:

$$ $D=\sup_{x}|F_{1}(x)-F_{2}(x)|$ (6) $$

where $F_{1}(x)$ and $F_{2}(x)$ are the ECDFs of the two samples. A small $D$ value indicates similar distributions, while a large $D$ value indicates differences. The p-value indicates the probability that the observed difference is due to chance. A low p-value (typically ¡ 0.05, $\log\text{p-value}<-5.0$) suggests the distributions are significantly different, whereas a high p-value suggests no significant difference.

### D.3 Forget Qualtiy

Compared to previous work  Maini et al. (), we introduce additional metrics to more robustly evaluate the forget quality of unlearned models. Specifically, we include four metrics: KS-Test ($S_{KS}$), Exact Match ($S_{EM}$), MIA ($S_{MIA}$), and APE scores ($S_{APE}$). Since these metrics differ in scale and interpretation, we unify them through appropriate transformations and combine them into a weighted final score to comprehensively measure the forget quality.

As shown in appendix D.2, the ks-test score is negative and closer to 0 indicating higher forget quality. To ensure this score contributes meaningfully to the overall evaluation, we normalize the KS-test using min-max scaling as follows:

$$ $\hat{S}_{KS}=\frac{S_{KS}-\min(S_{KS})}{\max(S_{KS})-\min(S_{KS})}$ (7) $$

This normalization scales the KS-test to a range between 0 and 1, where higher values indicate better forget quality. To compute the overall forget quality score, we use a weighted combination of the normalized KS-test score and the other metrics. The final score $S$ is calculated as follows:

$$ $S=0.5\cdot\hat{S}_{KS}+0.15\cdot(1-S_{EM})+0.2\cdot(1-S_{MIA})+0.15\cdot(1-S_{ APE})$ (8) $$

By combining these metrics in a weighted average, the final score $S$ provides a comprehensive evaluation of forget quality, where higher values indicate better model performance in forgetting unlearned information.

## Appendix E Demonstration

We illustrate some demonstrations of the predictions generated by unlearned VLMs across diverse strategies in Figure 4.

Figure: Figure 4: Demonstration of various VLM unlearning strategies in our FIUBench.
Refer to caption: x4.png

## Appendix F Broader Impacts

Our work proposes a dataset and FIUBench for facilitating VLM
unlearning researches. Machine unlearning for VLMs has the potential to significantly impact various aspects of AI development and deployment. By enhancing privacy, reducing bias, and fostering trust, unlearning contributes to the creation of more ethical and responsible AI systems. However, it also presents technical challenges and requires careful consideration to ensure that the benefits are fully realized without compromising model performance or ethical standards.

## Appendix G Prompt for Generating QA Pairs

## Appendix H Prompt for GPT-Eval

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: Please reply my concerns mentioned in the Weaknesses part.
soundness: 3
strengths: 1. This paper addresses an important ethics problem of AI, i.e., to fulfill the right to be forgotten for VLM, which is underexplored relatively. To my understanding, few works have been done in the literature. 

2. The benchmark, together with the evaluation metrics, is validated, especially via the assessment of four baseline unlearning algorithms. The results imply that existing unlearning algorithms are far from being mature when considering both model utility and forget quality. 

3. The benchmark is good to foster the community’s further research on developing better unlearning methods for VLMs.
summary: This paper introduces an unlearning benchmark for Vision Language Models (VLMs) under the Right to be Forgotten setting. After defining the VLM unlearning tasks, this benchmark assigns a two-stage evaluation pipeline with a newly proposed Fictitious Facial Identity VQA dataset. The proposed benchmark offers a comprehensive evaluation by computing both forget quality and model utility, with further assessment under membership inference attack and adversarial privacy extraction. Another contribution of the work is its evaluating four baseline unlearning algorithms, which indicates that none of them achieve good unlearning performance considering both model utility and forget quality. In addition, the divergent performance of Preference Optimization with and without membership inference attacks underscores the importance of privacy attacks for robust evaluations. This benchmark is good to foster the community’s further research on developing better unlearning methods for VLMs under the setting of Right to be Forgotten.
weaknesses: I am confused by the dataset constructed. As described in Line 165~172, 400 faces are sampled, which are then divided into 400 clusters by using K-means algorithm. How can 400 faces clustered into 400 clusters? 

And, to me, a dataset with 400 faces is relatively small for evaluating the unlearning problem. I am also not convinced why only synthetic faces are used for this evaluation. Is there any difference between real faces and synthetic faces for this evaluation purpose?

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: My major concerns lie in the effectiveness of the proposed benchmark and the experiments. If you can well address these problems, I am happy to improve my rating.
soundness: 2
strengths: 1.	Unlike unlearning in LLMs, which primarily focuses on forgetting sensitive text information, unlearning in VLMs extends to both images and text. This paper formalizes VLM unlearning as the task of unlearning private image and text-paired information.
2.	To study privacy under the Right to be Forgotten scenario, a two-stage evaluation pipeline with Fictitious Facial Identity VQA dataset is proposed.
summary: This paper introduces Facial Identity Unlearning Benchmark (FIUBENCH), a VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearning algorithms under the Right to be Forgotten setting. Moreover, FIUBENCH further incorporates membership inference attacks and adversarial privacy extraction to robustly evaluate unlearning performance, testing whether the private information is unlearned even under attacks.
weaknesses: 1.	This paper proposes FIUBENCH, a VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearning algorithms under the Right to be Forgotten setting, which is interesting. However, the effectiveness of this benchmark is unclear.
2.	Since the faces are generated by StyleGAN2, it is necessary to evaluate the distance between the generated face distribution and the real one. From the figure 1, the synthetic face images seem different from the real faces. Will it hurt the evaluations on the Vision Language models.
3.	For the experiments, you’d better involve more Vision Language models for evaluations.

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 4
questions: Please refer to the Strengths and Weaknesses.
soundness: 3
strengths: This paper systematically examines forgetting in Vision Language Models and introduces FIUBENCH, a new benchmark for robust evaluation of unlearning algorithms.
The paper is well-written and easy to understand.
summary: This paper introduces FIUBENCH, a benchmark designed to evaluate unlearning algorithms for Vision Language Models (VLMs) under the Right to be Forgotten setting. FIUBENCH includes a Fictitious Facial Identity VQA dataset and a two-stage evaluation pipeline to control information exposure levels. To handle VLMs’ ability to process semantically similar queries, FIUBENCH incorporates robust evaluation metrics, including membership inference and adversarial privacy attacks. Initial results on four baseline algorithms show limitations in unlearning performance, with trade-offs between model utility and forget accuracy.
weaknesses: The proposed benchmark uses a forget set and a retain set to assess the forgetting quality and model utility of unlearning algorithms. However, is this setting appropriate? In my view, the privacy concerns in Vision Language Models are more about forgetting specific sensitive information, such as identity or email, rather than simply forgetting individual samples.
The forget set is limited to 5% of the total dataset, comprising only 20 images. Could you explain the rationale behind selecting this specific proportion? How was this number determined?

---

confidence: 5
contribution: 3
initial_rating: 5
presentation: 3
questions: As mentioned in weakness.
soundness: 3
strengths: A novel dataset, Facial Identity Unlearning Benchmark (FIUBENCH) is constructed which can support the evaluation of the ‘Right to be Forgotten’ in Visual Language Model. Possible privacy risks are avoided through fictitious facial images. This submission is well written and easy to follow. The protocol provides settings to support different kinds of evaluations, including membership inference attack and adversarial privacy attack, etc.
summary: This submission touches an important privacy-related topic in vision language model - the forgetting of specified content, e.g. unlearning. To evaluate the performance of unlearning, the authors construct a Facial Identity Unlearning Benchmark (FIUBENCH), with protocol and several methods as baseline. This is an interesting work. From the reviewer's point of view, this is still a preliminary work in considering of dataset size, and the methods of evaluation and face generation. However, it is worthwhile to continue to advance it to become a consensus for the community.
weaknesses: The database size is too small to fit the task of evaluate the performance of unlearning in the wild. Although the fictitious facial images avoid the privacy risk, the synthetic images bring some flaws, such as artifacts in style, and these could become an unexpected feature for recognition. In addition the database has taken some action to ‘Filtering out similar faces with K-means’, which leads to an imposed environment for face recognition, and make the evaluation is far from real world case.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. My first question is whether it is necessary to store individual private information within VLMs in real-world use cases. In practical applications, the best approach is to separate individual private information from the VLM and use retrieval-augmented generation (RAG) techniques to provide appropriate responses. Under such techniques, the Right to be Forgotten can be easily ensured by deleting individual private information from the relevant databases. The authors need to further elaborate on the motivation for their research.
2. In line 290, it is described that the score range for GPT-Eval is 0-1, but in Table 1 and Table 2, there are scores that exceed this range. This appears to be an error.
3. Line 366 mentions that “early stopping based on training loss” is employed. Are the results reported in Table 2 based on this early stopping setting? What criteria are used for early stopping? I would like to know more details.
4. Unlearning algorithms involve a trade-off between model utility and forget quality. It might be necessary to compare the forget quality scores at fixed model utility levels, or vice versa. Alternatively, plotting a Model Utility-Forget Quality curve could be more informative. In fact, in Figure 3, representing the effectiveness of the unlearning algorithm with a single point is also unreasonable; a Model Utility-Forget Quality curve would likely be a more appropriate choice.
5. On certain metrics, the VLM category significantly affects the model utility and forget quality of an unlearning algorithm. Why is this the case? Comparing the performance of the unlearning algorithm with the Retain Model reveals many such instances: (1) The difference between the Retain Model and GA for LLaVA-Phi-mini-3B is 42.4 (93.7 v.s. 50.6), whereas, for LLama-3.2-Vision-Instruct-11B, the difference is 84.5 (88.8 v.s. 4.30). (2) The difference between the Retain Model and KL for LLaVA-Phi-mini-3B is -29.8 (12.3 v.s. 42.1), whereas, for LLama-3.2-Vision-Instruct-11B, the difference is -1.5 (12.2 v.s. 13.7). The significant impact of the VLM category on certain metrics raises the question of whether these metrics can provide robust testing results. Please provide a more detailed discussion on this matter.
6. In line 464, it is stated that you "finally decided to fine-tune the parameters from LLM." However, from Table 3, it is evident that the MIA of $E_{x_3}$ is the highest among the four fine-tuning strategies. This choice seems to lack sufficient justification.
soundness: 2
strengths: Based on the "Right to be Forgotten" setting, this paper defines a new VLM unlearning scenario closer to real-world use cases. 
The writing is clear.
summary: This paper, based on the Right to be Forgotten setting, defines a VLM unlearning scenario that is closer to real-world use cases. The primary contributions are as follows:
1. Formalizing the VLM unlearning tasks. It emphasizes that VLM unlearning algorithms should focus on sensitive information linked to images rather than the visual attributes themselves.
2. Defining a two-stage evaluation pipeline with the Fictitious Facial Identity VQA dataset. In the first stage, personal information is injected into the VLM, and in the second stage, the unlearning algorithm is performed.
3. Providing various metrics for robust evaluation in terms of forget quality and model utility.
weaknesses: 1. The rationale behind the research motivation requires further substantiation (Q1).
2. Some experimental details are not clearly described or potentially contain errors. (Q2, Q3, Q6). 
3. The analysis of the experimental results does not sufficiently consider the characteristics of unlearning algorithms, namely, the trade-off of model utility and forget quality for unlearning algorithms (Q4). 
4. Some metrics appear to lack robustness when the VLM category changes (Q5).

Meta-review: The paper introduces FIUBENCH, a benchmark for evaluating unlearning algorithms in Vision-Language Models (VLMs) under the "Right to be Forgotten" setting, using synthetic facial data and a two-stage evaluation pipeline. There are also some weaknesses of this paper, including the small dataset size, reliance on synthetic faces with potential artifacts, and unclear rationale for some experimental design choices. Despite these limitations, the paper’s novelty and relevance to a growing privacy challenge justify its acceptance, as it lays a foundation for future research in VLM unlearning.

Decision: Accept (Poster)