Title: Cross-Modality Perturbation Synergy Attack for Person Re-identification
Venue: NeurIPS 2024
Accepted: yes
Keywords: Computer Vision; Adversarial Attack；Person Re-identification
Abstract: In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems.

Cross-Modality Perturbation Synergy Attack for Person Re-identification

Abstract

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems.

1 Introduction

With the rapid advancement of surveillance technology, person re-identification (ReID) [1-4] has emerged as a pivotal component in the realm of security, garnering escalating attention. ReID constitutes a fundamental task in computer vision [5-9], aiming to precisely identify the same individual across diverse locations and time points by analyzing pedestrian images captured through surveillance cameras [10]. The challenges inherent in this task encompass factors such as changes in viewpoint, lighting conditions [11, 12], occlusion [13, 14], and pose variations, culminating in significant appearance variations of the same individual across distinct camera views [15].

In traditional ReID, where samples are image-based, the conventional methodology centers on matching visible to visible (RGB to RGB) data. However, when dealing with diverse scenarios and

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conditions, especially involving multiple image modalities such as RGB and infrared images, the system needs to intricately handle the differences in images from different modalities [18-20]. This is essential to ensure that the system exhibits better robustness across different modalities. Hence, cross-modality ReID is considered more challenging due to the need for addressing these modality differences [21, 22].

Cross-modal ReID [23, 24, 21, 25] plays a crucial role in significantly expanding the applicability of traditional ReID methods, focusing on addressing complex matching issues between different image modalities. In practical surveillance systems, the simultaneous use of multiple sensors, such as RGB cameras and infrared cameras, is a common scenario. This task requires innovative solutions to effectively bridge the differences between various modalities, ensuring robust and accurate reidentification of pedestrians in heterogeneous sensor outputs.

Currently, most research on the security of ReID focuses on single-modality systems based on RGB images [26-31], while the security of cross-modality ReID systems has received insufficient attention. The challenge in cross-modality attacks arises from significant visual differences among different modality inputs, requiring attackers to effectively capture shared features from each modality for perturbation implementation. However, as shown in Fig. 1, existing attack methods in crossmodal scenarios require optimizing perturbations separately for each modality, lacking an intrinsic mechanism to capture shared knowledge between different modalities, which limits the success rate of the attacks. To address this issue, we propose a synergistic optimization method combined with triplet loss, utilizing information from different modalities to optimize the universal perturbation. This method pushes the features of different samples into a common sub-region that affects the model's accuracy, as shown in Fig. 2.

Specifically, we propose the Cross-Modality Perturbation Synergy (CMPS) method, a universal perturbation approach designed specifically for cross-modality ReID systems. This method simultaneously leverages gradient information from multiple modalities to jointly optimize universal perturbations across visible and infrared images. CMPS incorporates cross-modality triplet loss to ensure feature consistency across different modalities, enhancing the generality of the perturbation. During the synergistic optimization process, CMPS iteratively updates gradients from various modalities within a unified optimization framework, effectively capturing and utilizing shared features across modalities. To further reduce visual differences between modalities, we introduce cross-modality attack augmentation, converting images into grayscale to standardize their visual representation and facilitate the learning of modality-agnostic perturbations. As a result, these universal perturbations push the features of different samples toward a common region in the feature space, significantly diminishing the model's ability to accurately distinguish identities in cross-modality scenarios, thereby successfully deceiving the model.

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In our experiments on widely utilized cross-modality ReID datasets, including RegDB [32], SYSU [33] and LLCM [24], we not only showcase the effectiveness of our proposed method but also provide insights for fortifying the robustness of cross-modality ReID systems in the future. This research contributes by bridging gaps in current studies and introducing novel perspectives to study the security challenges in cross-modality ReID systems.

The main contributions of our work can be summarized as:

To the best of our knowledge, our work is the first to investigate vulnerabilities in cross-modality ReID models. By explicitly incorporating cross-modality constraints into the synergistic optimization process, we enhance the universality of the learned cross-modality perturbations. Additionally, we provide mathematical analysis to demonstrate the superiority of our proposed method over traditional approaches.

We propose a cross-modality attack augmentation method, utilizing random grayscale transformations to narrow the gap between different modalities, aiding our cross-modality perturbation synergy attack in better capturing shared features across modalities.

Extensive experiments conducted on three widely used cross-modality ReID benchmarks demonstrate the effectiveness of our proposed cross-modality attack. Our method exhibits good transferability even when attacking different models. The code will be available at https://github.com/ finger-monkey/cmps__attack .

2 Related Works

Adversarial Attack. Adversarial attacks are a technique involving the clever design of small input perturbations with the aim of deceiving machine learning models, leading them to produce misleading outputs. This form of attack is not confined to the image domain but extends to models in various fields, including speech [34] and text [35-37]. Typically, the goal of adversarial attacks is to tweak input data in a way that causes the model to make erroneous predictions when handling these subtly modified samples [16, 38-40]. In the early stages of research, adversarial attacks had to

be customized for each specific sample. However, with the evolution of related studies, universal perturbation [41] attacks were introduced, aiming to find perturbations effective across multiple samples rather than tailored to individual instances. Research on universal perturbation attacks seeks to expose vulnerabilities in models, prompting designers to enhance their robustness to withstand a broader range of adversarial challenges.

Adversarial Attacks in ReID. Some ReID attack methods have been proposed, with current research predominantly focusing on RGB-RGB matching. These methods mainly include: Metric-FGSM [29] extends some techniques, inspired by classification attacks, into a category known as metric attacks. These encompass Fast Gradient Sign Method (FGSM) [16], Iterative FGSM (IFGSM), and Momentum IFGSM (MIFGSM) [42]. The Furthest-Negative Attack (FNA) [30] integrates hard sample mining [43] and triple loss to employ pushing and pulling guides. These guides guide image features towards the least similar cluster while moving away from other similar features. Deep Mis-Ranking (DMR) [31] utilizes a multi-stage network architecture to pyramidally extract features at different levels, aiming to derive general and transferable features for adversarial perturbations. Gong et al. [28] proposed a local transformation attack (LTA) method specifically aimed at attacking color features without requiring additional reference images, and discussed effective defense strategies against current ReID attacks. The Opposite-direction Feature Attack (ODFA) [26] exploits featurelevel adversarial gradients to generate examples that guide features in the opposite direction with an artificial guide. Yang et al. [27] introduced a combined attack named Col.+Del., which integrates UAP-Retrieval [44] with color space perturbations [45]. While this method also explores universal perturbations in ReID, its generality is limited due to the inability to leverage color information in cross-modality problems and the lack of a mechanism for associating different modality information. In contrast to the aforementioned approaches, our focus lies on addressing cross-modality challenges.

Algorithm 1 Procedure of CMPS attack

3 Methodology

In this section, we introduce a universal perturbation designed for cross-modality attacks, referred to as the Cross-Modality Perturbation Synergy (CMPS) attack. Considering the significant differences between different modalities, we propose a attack augmentation method to bridge the gap between modalities, aiding in enhancing the perturbation's universality across different modalities. Our

objective in addressing this problem is to find a universal adversarial perturbation, denoted as η , capable of misleading the retrieval ranking results of cross-modality ReID models. The adversarial operation involves adding η to a query image I . The perturbed query image, denoted as I adv = I + η , is then used to retrieve from the gallery and deceive the cross-modality ReID model f . The algorithm is summarized in Alg. 1.

3.1 Overall Framework

In Fig. 2, we illustrate the overall framework of the proposed CMPS attack. During the training phase, we optimize η using our cross-modality attack augmentation method, which leverages images from different modalities to bridge their inherent differences and enhance cross-modality universality. In the attack phase, the optimized η deceives reID models, leading to inaccurate ranking lists. Section 3.2 outlines the framework and overall optimization objective, providing a macro-level overview. Section 3.4 delves into the specific process of perturbation optimization across different modalities.

3.2 Optimizing Loss Functions for Attacking

Our study aims to deceive cross-modality ReID models using a universal perturbation. We have specifically designed a triplet loss tailored for our proposed attack method, which can correlate different modalities and influence the distance relationships between images from different modalities.

We follow the approach of [44] to optimize the perturbation using cluster centroids. This method directly impacts the similarity between pedestrian identities in the ReID model's feature space (rather than the similarity between individual samples), making it more effective. Subsequently, leveraging the acquired cluster centroids, we apply our triplet loss to distort the pairwise relations between pedestrian identities. This process can be represented as follows:

As shown in Fig. 3, the loss function mentioned above fully leverages the triplet-wise relationships across different modality. Through this loss, we are able to pull the negative samples of each modality closer to the adversarial samples and push the positive samples of each modality away from the adversarial samples. Here, C p RGB and C n RGB represent the cluster centroids of the positive samples to push and negative samples to pull, respectively, in the original visible (RGB) image feature space of the training data. Similar definitions apply to other modalities. f adv RGB , f adv g , and f adv ir denote the perturbed features of the disturbed image in the visible, grayscale, and infrared (or thermal) modalities, respectively.

3.3 Cross-Modality Attack Augmentation Method

Intuitively, as illustrated in Fig. 4, maximizing the overlap of common factors across different modalities facilitates the capture of shared features by the learned perturbation. Grayscale images, being inherently homogeneous, serve as effective mediators between diverse modalities. Consequently, we introduce random grayscale transformations into adversarial attack methods, referred to as CrossModality Attack Augmentation. This approach guides cross-modality perturbations by leveraging homogeneous grayscale images sourced from diverse modalities. The primary objective is to explore the underlying structural relationships across heterogeneous modalities.

The process of grayscale transformation can be represented as follows:

The function t(·) represents the grayscale transformation using ITU-R BT.601-7 standard weights, combining the RGB channels of each pixel into a single grayscale channel. From this, we construct a 3-channel grayscale image x g by replicating the grayscale channel:

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To synergistically utilize gradient information from diverse modalities for perturbation optimization, narrow the gap between different modalities to better capture shared knowledge, we adopt the following training process to generate a universal perturbation:

(1) Learning the visible modality. For a given batch of visible images with n samples, we extract and perturb their features using the cross-modality ReID model. We update the temporary perturbation η iteratively using Momentum-Inertia Stochastic Gradient Descent (MI-SGD), expressed as:

Here, θ represents the momentum value (set as θ = 1 ), and ∆ ′ ir is derived from the previous iteration. The iteration step size is denoted by α (set as α = ϵ 12 ), where ϵ is the adversarial bound ( ϵ = 8 , unless otherwise specified). We set the margin ρ = 0 . 5 in our triplet loss.

(2) Learning the grayscale modality. This part is executed through data augmentation. It is not considered as a separate module and is therefore not explicitly listed in Alg. 1. Specifically, during the perturbation learning process, we randomly transform visible or infrared (or thermal) images into homogeneous grayscale images, participating in the iterative optimization of adversarial perturbations. It is employed to bridge the gap between different modalities, thereby improving the universality of the perturbation across diverse modalities. In order to investigate the impact of different grayscale conversion probabilities on attack performance, we conducted a series of ablation experiments. For details, please refer to Fig. 5 in supplementary material.

(3) Learning the infrared (or thermal) modality. This step is similar to (1). We utilize the infrared (or thermal) images to learn the perturbation η with the our loss functions:

Here, ∆ RGB derived from step (1). The main difference compared to the previous step lies in the perturbation applied to the input and the gradients related to momentum.

Theoretical Analysis. In traditional optimization, optimizing for one modality can render the perturbation suboptimal for the other, leading to a bias toward a single modality. In contrast, the proposed aggregated optimization method jointly optimizes both modalities, ultimately identifying a universal perturbation that enhances cross-modality attack performance. In the supplementary material 7, we provide a mathematical analysis demonstrating the effectiveness of this method compared to traditional attack methods that lack intrinsic correlations between different modalities.

4 Experiments

In this section, we compare our approach with several methods, including traditional classification attack methods FGSM [16] and PGD [17], traditional metric attack methods like Metric-FGSM [29], as well as state-of-the-art ReID attack methods such as LTA [28] * , ODFA[26] and Col.+Del.[27].

Datasets . We evaluate our proposed method on two commonly used cross-modality ReID datasets: SYSU-MM01 [33], RegDB [32] and LLCM [24]. SYSU-MM01 is a large-scale dataset with 395 training identities, captured by 6 cameras (4 RGB, 2 near-infrared) on the SYSU campus. It comprises 22,258 visible and 11,909 near-infrared images. The testing set consists of 95 identities with two evaluation settings. The query sets include 3803 images from two IR cameras. We conduct ten trials following established methods [46] and report the average retrieval performance. Please refer to [33] for the evaluation protocol. RegDB [32] is a smaller-scale dataset with 412 identities, each having ten visible and ten thermal images. we randomly select 206 identities (2,060 images) for training and use the remaining 206 identities (2,060 images) for testing. LLCM is a dataset designed specifically for cross-modality ReID in low-light environments. Compared to other datasets, its diverse scenarios and low-light conditions present greater challenges for attackers. This complexity and uncertainty make adversarial attacks more difficult to execute. We assess our model in two retrieval scenarios: visible-thermal and thermal-visible performance.

Evaluation Metrics . Following existing works [47], we employ Rank-k precision and Cumulative Matching Characteristics (CMC) and mean Average Precision (mAP) as evaluation metrics. Rank-1 represents the average accuracy of the top-ranked result corresponding to each cross-modality query image. mAP represents the mean average accuracy, where the query results are sorted based on similarity, and the closer the correct result is to the top of the list, the higher the precision. Please note that, for adversarial attacks, a lower accuracy indicates a more successful attack.

4.1 Performance on Cross-Modality ReID

We used AGW [21] and DDAG [25] as baseline models for testing on the RegDB and SYSU cross-modality ReID datasets. AGW (Attention Generalized mean pooling with Weighted triplet loss) enhances the learning capability of crucial features by integrating non-local attention blocks, learnable GeM pooling, and weighted regularization triplet loss. DDAG (Dynamic Dual-Attentive Aggregation) improves feature learning by combining intra-modality weighted-part attention and cross-modality graph structured attention, considering both part-level and cross-modal contextual cues. Additionally, we use DEEN [24] (Diverse Embedding Expansion Network) as baseline models for testing on the LLCM [24] cross-modality ReID datasets. The core idea of DEEN is to enhance the feature representation capability by introducing a diversity embedding mechanism. The network expands the embedding space, allowing features from visible and infrared images to align better in a high-dimensional space, thereby improving the accuracy of cross-modality matching.

The experiments encompass two scenarios: 1) Perturbing visible images (query) to disrupt the retrieval of infrared or thermal non-visible images (gallery). This is denoted as "Visible to Infrared" in Tab.1 and "Visible to Thermal" in Tab.2. 2) Perturbing infrared or thermal non-visible images (query) to interfere with the retrieval of visible images (gallery). This is indicated as "Infrared to Visible" in Tab.1 and "Thermal to Visible" in Tab.2.

From Tab.1, it can be seen that the proposed method reduces the rank-1 accuracy to below 2% in both the 'Visible to Infrared' and 'Infrared to Visible' cases. Similarly, from Tab.2, the rank-1 accuracy drops below 3% in both the 'Visible to Thermal' and 'Thermal to Visible' scenarios. In contrast, traditional metric-based attacks, such as Metric-FGSM (M-FGSM)[29], LTA [28] and ODFA[26], lead to attacked models with significantly higher rank-1 accuracy, whereas traditional classification attacks (such as FGSM [16] and PGD [17]) perform even worse, with rank-1 accuracy remaining over 60%. This is because ReID relies on metric learning for feature matching rather than category classification, requiring attacks specifically tailored for metric learning. These results indicate that, compared to traditional methods that optimize perturbations separately for each modality without considering the inherent correlations between different modalities, our proposed approach demonstrates significant attacking effectiveness across different modalities.

Comparison with State-of-the-Art. Col.+Del., as a universal perturbation method, was fairly compared by first optimizing with one modality's dataset and then fine-tuning with the other modality. Since universal perturbations capture shared patterns across the entire data distribution, Col.+Del. is

capable of achieving some level of attack effectiveness in cross-modality scenarios. However, by comparing Tab.1, Tab.2, and Tab.3, we observe that although Col.+Del. performs better than other methods, its effectiveness is still noticeably limited due to the lack of intrinsic correlation mechanisms between modalities. Moreover, as shown in Fig.6, our method outperforms Col.+Del. in transfer attacks across different baselines in cross-modality ReID. The conclusions from these experiments are as follows: 1) In cross-modality attacks, Col.+Del. demonstrates the feasibility of universal perturbations. However, its performance is limited by its failure to account for modality differences and inherent correlations. 2) Our method better bridges the gap between different modalities, more effectively capturing shared features across them.

4.2 Transferability of CMPS

From Fig.6 in supplemental material, the results of the proposed method's transfer attacks on two baseline models, AGW and DDAG, can be observed. For example, on the SYSU dataset, the original attack result of the proposed method on DDAG is mAP=1.84% (refer to Tab. 1). When the perturbation is transferred from AGW to DDAG, the attack result becomes mAP=3.41%. This indicates that the proposed attack method exhibits good generalization across different models, and thus, the attack performance does not degrade significantly. This consistent result is observed on both the RegDB and SYSU datasets. Similarly, in Fig.7 of the supplemental material, we evaluate the cross-dataset transferability of perturbations in comparison with Col.+Del. The results demonstrate a significant advantage of our method. Additionally, we conducted adversarial transferability experiments on IDE [48], PCB [49], and ResNet18 [50]. The rank-1 transfer attack success rates are presented in Tab.4. It can be observed that our method consistently achieves higher transfer attack success rates across all model combinations compared to Col.+Del., indicating that our method demonstrates stronger robustness in generating more universal adversarial perturbations.

4.3 Ablation Study

Our method is implemented based on UAP-Retrieval [44]. To validate the effectiveness of the proposed method, we conducted experiments by adding augmentation (Cross-Modality Attack Augmentation) and CMPS to the baseline. Results with AGW baseline model are reported in Tab. 5. The No.1 line represents the UAP-Retrieval algorithm. In the table, 'Aug' indicates the use of the Cross-Modality Attack Augmentation proposed in this paper.

The effectiveness of CMPS . Comparing No.1 with No.3 and No.4, we observe the following: 1) The direct use of UAP-Retrievals yields limited performance. 2) Training with the CMPS strategy

proposed in this paper consistently improves the performance of attack results and the universality of learned perturbations.

The effectiveness of augmentation method . Our approach includes cross-modality attack augmentation. Comparing results of No.1, No.2, and No.4 shows its benefits. For example, on the RegDB dataset, augmentation (No.2) reduces mAP from 6.87% to 5.11%, 1.76% lower than without augmentation (No.1). Similarly, with CMPS, mAP drops from 3.98% to 3.46% (No.4), a 0.52% decrease compared to No.3. These findings suggest that using appropriate augmentation enhances cross-modality ReID adversarial attacks' universality. If not specified, our experiments default to using CMPS augmentation. Fig. 5 in the supplementary materials displays the experimental results of our augmentation performed at different probabilities. It can be observed that when the probability value is around 20%, it achieves optimal effectiveness in assisting the attack. If not specified, a probability value of 20% for augmentation is used by default in experiments.

Impact of adversarial boundary size. We conducted an ablation study on different adversarial boundary sizes ( ϵ ), as shown in the supplementary material 6. In practical applications, ϵ is typically kept moderate to balance perturbation visibility and attack effectiveness. To maintain consistency with previous work [27], we set ϵ = 8 for comparison unless otherwise specified.

5 Conclusion

In this study, we have proposed a cross-modality attack method known as Cross-Modality Perturbation Synergy (CMPS) attack, aimed at evaluating the security of cross-modality ReID systems. The core idea behind the CMPS attack is to capture shared knowledge between visible and non-visible images to optimize perturbations. Additionally, we proposed a Cross-Modality Attack Augmentation method, utilizing grayscale images to bridge the gap between different modalities, further enhancing the attack performance. Through experiments conducted on the RegDB, SYSU and LLCM datasets, we demonstrated the effectiveness of the proposed method while also revealing the limitations of traditional attack approaches. The primary objective of this study has been to assess the security of cross-modality ReID systems. In future research, on the one hand, we will continue to improve the transferability of cross-modality attacks across different datasets and models; on the other hand, we plan to develop robust ReID methods specifically tailored for cross-modality attacks, aimed at defending against adversarial samples. This study not only contributes to advancing the understanding of the security of cross-modality ReID systems but also provides strong motivation for ensuring the reliability and security of these systems in real-world applications.

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant No.62276222 and the Public Technology Service Platform Project of Xiamen City, Grant No.3502Z20231043.

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Contents

Supplemental Material

6 Supplemental Experiments

Our experiments were conducted using three RTX 2080 Ti GPUs, each with 11GB of memory.

Metrics

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Our attack

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Comparison of Transferability

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7 Proof of Method Superioritys

We design a cross-modality triplet loss to simultaneously optimize two modalities, which effectively captures common features between different modalities and enhances the cross-modality adaptability of universal perturbations.

7.1 Definition of Cross-Modality Triplet Loss

The cross-modality triplet loss aims to optimize the model by adjusting the distance relationships among triplet samples (anchor, positive, negative) so that samples of the same identity are closer, while samples of different identities are farther apart. Specifically, given samples ( x A , x P , x N ) , where:

x A is the anchor sample,

x P is the positive sample with the same identity as the anchor (from a different modality),

x N is the negative sample with a different identity from the anchor.

The triplet loss function is defined as:

where D ( · , · ) denotes the distance metric (e.g., Euclidean distance), and α is a margin hyperparameter.

Mathematically, given the cross-modality triplet loss:

We can view it as part of the sum of the loss functions for two modalities:

Thus, the overall optimization objective can be expressed as:

This form effectively aggregates the losses of different modalities, thereby optimizing the loss functions of different modalities simultaneously, achieving joint optimization of cross-modality data. This approach trains universal perturbations with better generalization capabilities than methods that consider only single-modality information.

7.2 Proof of Aggregated Optimization Superiority

Assume we have data from two modalities: modality A and modality B. Let L A ( η ) and L B ( η ) be the loss functions on modality A and modality B, respectively. The objective of single-modality training is:

The stepwise optimization method first optimizes L A ( η ) and then optimizes L B ( η ) :

The aggregated optimization of the two loss functions is:

Using the gradient aggregation method, it can be expressed as:

Next, we consider the different optimization paths of the two methods.

7.2.1 Stepwise Optimization Method

The stepwise optimization method first optimizes the loss function of modality A and then the loss function of modality B. Assume the update rule at iteration k is:

After optimizing the loss function of modality A, the loss function of modality B is optimized:

Since the two optimization processes are separate, this may result in η being optimal for modality A but not necessarily for modality B.

7.2.2 Aggregated Optimization Method

The aggregated optimization method considers the losses of both modalities in each iteration. Assume the update rule at iteration k is:

In this way, each update considers the losses of both modalities, ensuring that η approaches the optimal solution for both modalities.

To further prove that the aggregated optimization method can find a better perturbation η , we can analyze the existence and uniqueness of the optimal solution.

Assume L A ( η ) and L B ( η ) are continuously differentiable and convex loss functions. According to convex optimization theory, the optimal solutions of the loss functions exist and are unique.

The optimal solution of the stepwise optimization method is:

where η ∗ is the optimal solution of L A ( η ) .

The optimal solution of the aggregated optimization method is:

Since η ∗ step is not necessarily globally optimal for modality B, and η ∗ agg is the global optimal solution considering both modalities, we can derive:

7.3 Generalization Error Analysis

Generalization error measures the model's performance on unseen data. We can further prove the superiority of aggregated training through generalization error analysis.

Let L train and L test be the losses on the training and test sets, respectively. The generalization error is defined as:

The upper bound of the generalization error can be expressed using measures such as Rademacher complexity or VC dimension. For machine learning models, the lower the model complexity, the smaller the generalization error. Simultaneously optimizing the losses for multiple tasks (modalities) can reduce overfitting to a single task (modality), as the model needs to perform well on multiple tasks (modalities) simultaneously. This effectively introduces an implicit regularization effect, reducing the model complexity. Therefore, compared to the stepwise optimization method, the aggregated optimization method can effectively reduce the complexity of the perturbation model. The lower the model complexity, the smaller the generalization error.

The Rademacher complexity measures the complexity of a class of models on a given sample set. For a function h in the hypothesis space H , the empirical Rademacher complexity on n samples is defined as:

where σ i are Rademacher random variables, taking values ± 1 with equal probability.

The impact of modality aggregation on complexity:

Assume H A and H B are the hypothesis spaces of modality A and modality B, respectively. The stepwise optimization method first optimizes H A and then H B. Its empirical Rademacher complexity can be expressed as:

The aggregated optimization method optimizes H A ∪ H B simultaneously. Its empirical Rademacher complexity is:

According to the properties of Rademacher complexity, the complexity of H A ∪H B is usually less than or equal to the sum of the complexities of H A and H B:

Generalization error upper bound derivation:

Using Rademacher complexity, we can derive the upper bound of the generalization error. For the loss function L and hypothesis space H , the upper bound of the generalization error is:

where L◦H denotes the composition of the loss function with the hypothesis space.

The upper bound of the generalization error for the stepwise optimization method is:

The upper bound of the generalization error for the aggregated optimization method is:

This indicates that the aggregated optimization method has a lower upper bound on the generalization error compared to the stepwise optimization method.

Since

Therefore:

8 Discussion

8.1 Ethical Considerations

In this study, we introduce a novel cross-modal adversarial attack method known as Cross-Modality Perturbation Synergy (CMPS). This research offers a new perspective on understanding and enhancing the security of cross-modal ReID systems by leveraging shared features across different modalities to optimize perturbations. However, this approach also raises a series of ethical and safety concerns regarding the potential negative impacts of adversarial attack techniques. The CMPS method, like other adversarial technologies, can be maliciously exploited, posing a serious threat to public safety.

However, we recognize the positive value of adversarial attack research. It reveals vulnerabilities in existing systems, prompting academia and industry to make in-depth improvements to the robustness of machine learning models. The positive impact of this study lies in its potential to combine adversarial training with the attack methods presented to enhance system security and bring positive social impacts. Therefore, we emphasize the importance of conducting adversarial attack research within an ethical framework and encourage further development of defensive technologies to build a safer and more reliable technological environment.

8.2 Limitations and Future Work

Here, we need to acknowledge the limitations of the proposed method and identify potential directions for future research. Firstly, current attack techniques primarily focus on gradient-based perturbation optimization for given datasets. However, in real-world scenarios, the modalities encountered are often unknown and not limited to RGB, infrared, and thermal imaging. Moreover, effectively transferring perturbations to different and unknown modalities presents a significant research challenge.

When dealing with various models and modalities, gradient-based methods face several challenges. Firstly, these methods are prone to "catastrophic forgetting," where learning new information can lead to the loss of previously learned knowledge, affecting the effectiveness of perturbations. Secondly, the inconsistency of gradient information across multiple models and modalities can negatively impact the stability and generalizability of the method. Therefore, future research should explore more robust algorithms that can effectively operate in complex environments involving multiple modalities and models, thereby enhancing the applicability and transferability of attacks.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses security concerns in cross-modality person re-identification systems, focusing on systems that use both RGB and infrared images. Traditional ReID systems have primarily focused on RGB images, but the differences between RGB and infrared modalities present unique challenges. The authors propose a universal perturbation attack method designed for cross-modality ReID, which optimizes perturbations using gradients from multiple modalities. Experiments on the RegDB and SYSU-MM01 datasets demonstrate the effectiveness of this method.\n\nSTRENGTHS:\n1.This work investigates vulnerabilities in cross-modality ReID models.\n2.Proposes a cross-modality perturbation synergy (CMPS) attack using triplet loss to optimize perturbations that leverage shared knowledge across modalities.\n3.Extensive experiments on the RegDB and SYSU datasets show the method's effectiveness and provide insights for future improvements in cross-modality ReID robustness.\n\nWEAKNESSES:\n1.The authors use random grayscale transformations to reduce modality differences. Are the three-channel grayscale images based on visible light, infrared, or both?\n2.What is the intended meaning of the decision boundary in Figure 2?\n3.The Figure 3 is difficult to understand.\n4.Can the method be discussed on more datasets, such as the LLCM dataset?\n5.Does the size of the adversarial boundary affect the experimental results?\n6.What is the overall loss function used in the paper? How are the functions discussed in Section 3.2 and Section 3.4 related? Additionally, in Section 3.4, the sequence of formulas seems inconsistent with the context.\n7. The SYSU-MM01 common tests are conducted in all-search and indoor-search modes. Which mode is the experiment in Table 1 based on? It is recommended to discuss both modes.\n8.The authors should select more diverse types of baseline models to verify the generalizability of the method.\n9.In Algorithm 1, the CMPS attack is mentioned to use grayscale images to update perturbations, but in the ablation experiments in Table 3, they are validated as two separate modules. What is the reason for this?\n\nQUESTIONS:\nSee the weaknesses above.\n\nLIMITATIONS:\nThe authors explain the limitations of their work, and there is no negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates adversarial attacks on cross-modality person re-identification (ReID) systems. It is purportedly the first study to investigate vulnerabilities in cross-modality ReID models, with the goal of evaluating the security of these systems. \n\nTo this end, the paper introduces an innovative universal perturbation attack method specifically designed for cross-modality ReID. This new method includes a cross-modality attack augmentation technique, which helps the perturbation synergy attack to better bridge the gap between different modalities. Experimental results on the RegDB and SYSU datasets show that the CMPS attack significantly reduces the accuracy of ReID systems, outperforming existing traditional methods.\n\nSTRENGTHS:\nThis paper introduces a novel perturbation attack specifically designed for cross-modality person re-identification, addressing a significant gap in the existing literature. The authors have made a notable contribution by being the first to explore vulnerabilities in these cross-modality systems. The importance of this work lies in its innovative approach to tackling the complex challenges posed by person re-identification systems that use different imaging modalities, such as RGB and infrared. The paper provides rigorous theoretical analysis and thorough experimental validation of the proposed method, demonstrating its significance both theoretically and practically. Additionally, the quality of writing is high, with the authors presenting their ideas clearly and concisely, making it accessible even to readers who are not experts in the field.\n\nWEAKNESSES:\n1. The difference between VI-ReID and regular ReID is that VI-ReID requires matching pedestrians across different modalities. Theoretically, it is sufficient to attack the RGB features, rendering them inadequate to match the infrared image features. Can focusing solely on attacking RGB features also fulfill the requirements?\n2. The article states that infrared images are grayscale images, which might not be entirely accurate. Near-infrared images appear visually similar to grayscale images and lack color information.\n3. While the paper demonstrates the effectiveness of the CMPS attack in bridging the gap between RGB and infrared modalities, extending this research to a broader range of modalities would provide deeper insights into the applicability and limitations of the proposed attack method. Additionally, the current experiments are primarily focused on the RegDB and SYSU datasets. Future studies incorporating more diverse datasets from various scenarios and conditions would help validate the robustness and generalizability of this method in complex environments.\n\nQUESTIONS:\nHave the authors considered how to enhance the robustness of cross-modality ReID systems to resist attacks similar to CMPS? The paper could further expand its impact by investigating the resilience of CMPS attacks against existing adversarial defense mechanisms. By discussing or empirically evaluating the performance of CMPS attacks in the presence of adversarial training or other defense strategies, a deeper understanding of the proposed method's robustness can be gained, guiding the development of more secure cross-modality ReID systems.\n\nLIMITATIONS:\nYes, the authors have adequately addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper is the first to explore the security vulnerabilities of cross-modality ReID models and proposes a universal perturbation attack method for cross-modality person re-identification (ReID) systems, called the Cross-Modality Perturbation Synergy (CMPS) attack. This method innovatively utilizes gradient information from both RGB and infrared images to generate a universal perturbation, maintaining its effectiveness across multiple modalities. Experiments conducted on the RegDB and SYSU datasets demonstrate that the CMPS method significantly reduces the accuracy of ReID models, outperforming existing traditional attack methods. This study emphasizes the necessity of considering multiple modalities in the security evaluation of ReID systems and provides new perspectives for future research.\n\nSTRENGTHS:\nThis paper makes a notable contribution by addressing security challenges in cross-modality person re-identification (ReID) systems with a pioneering approach. It stands out as the first to explore vulnerabilities in these systems, advancing the understanding and testing of their robustness against adversarial attacks, particularly in under-explored cross-modality scenarios. By focusing on this critical gap, the paper opens new directions for security evaluation in complex multimodal environments.\n\nThe novelty of the proposed attack method lies in its innovative use of aggregated feature gradients from different modality images to probe vulnerabilities in cross-modality ReID models. The writing quality is excellent, featuring clear, concise, and well-structured explanations that effectively communicate the paper's concepts. The insights provided are poised to drive improvements in the security of ReID systems.\n\nWEAKNESSES:\n1. Figure 1 would benefit from using the same set of gallery images for both the before and after comparisons.\n\n2. The paper exhibits inconsistencies in referencing \"Table\" and \"Tab,\" as observed in lines 228 and 229. Standardizing these references would enhance readability and professionalism.\n\nQUESTIONS:\nThe paper offers initial insights into the transferability of the proposed attack across various models and datasets. A more thorough investigation into factors influencing perturbation transferability, such as diverse model architectures and characteristics of cross-modality data, would advance the understanding of adversarial attacks in this domain. Such comprehensive research could elucidate the performance of perturbations in different settings, optimizing attack methods and enhancing their practical applicability.\n\nLIMITATIONS:\nYes, the authors have appropriately addressed the limitations of their study.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an innovative strategy called Cross-Modality Perturbation Synergy (CMPS) attack, aimed at revealing security vulnerabilities in cross-modality person re-identification (ReID) systems. These systems are crucial in security applications, typically using RGB and infrared imaging to identify individuals under various lighting conditions and camera setups. The study highlights that current security research mainly focuses on single-modality (RGB-based) ReID systems, neglecting the complexities and potential vulnerabilities in cross-modality scenarios.\n\nSTRENGTHS:\n1. By focusing on cross-modality security vulnerabilities, this paper fills a critical gap in the field of person re-identification (ReID), where previous research has primarily concentrated on single-modality (RGB-based) studies. This novel perspective is highly significant as it extends the understanding of ReID systems to real-world scenarios where different modalities are frequently used.\n2. The proposed Cross-Modality Perturbation Synergy (CMPS) attack is innovative, leveraging gradient information from both RGB and infrared images to generate universal perturbations that remain effective across multiple modalities. This approach not only demonstrates theoretical originality but also provides practical value through robust experimental validation.\n3. The experimental results on widely used datasets such as RegDB and SYSU convincingly demonstrate the superiority of the CMPS method compared to existing traditional attack methods, highlighting the method's effectiveness in reducing the accuracy of ReID systems.\n4. This paper exhibits strong writing and organizational skills, with logically tight explanations that make complex concepts easy to understand. The comprehensive description and intuitive presentation of the methodology and experimental setup contribute to the paper's clarity and comprehensibility.\n\nWEAKNESSES:\n1. Although the paper is clearly written, some technical sections are still quite complex. Simplifying these parts would benefit a wider audience. For example, in the section \"3.2 Optimizing Loss Functions for Attacking,\" the derivation process from equations (4) to (11) is rather dense and might be difficult for non-specialist readers to understand. Adding more explanatory text between these equations would make it easier for a broader range of readers to follow.\n2. The descriptions of \"Figure\" and \"Fig\" in lines 53 and 55 of the article are inconsistent, and they need to be carefully checked and unified.\n\nQUESTIONS:\n1. The effectiveness of the CMPS attack method has been validated on RGB, infrared, and thermal images. Have the authors considered whether this method can be extended to more modalities, or even any modality? If so, what adjustments would be necessary to the current method?\n2. The paper presents an interesting model attack scenario where universal perturbations are added to query images to mislead ReID models. Have the authors considered how to deploy such interference in real-world ReID systems?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-08"}