Harmfully Manipulated Images Matter in Multimodal Misinformation Detection

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-07 14:28:29

Abstract: Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying misinformation, especially those containing multimodal content, has attracted growing attention from the academic and industrial communities, and induced an active research topic named Multimodal Misinformation Detection (MMD). Typically, existing MMD methods capture the semantic correlation and inconsistency between multiple modalities, but neglect some potential clues in multimodal content. Recent studies suggest that manipulated traces of the images in articles are non-trivial clues for detecting misinformation. Meanwhile, we find that the underlying intentions behind the manipulation, e.g., harmful and harmless, also matter in MMD. Accordingly, in this work, we propose to detect misinformation by learning manipulation features that indicate whether the image has been manipulated, as well as intention features regarding the harmful and harmless intentions of the manipulation. Unfortunately, the manipulation and intention labels that make these features discriminative are unknown. To overcome the problem, we propose two weakly supervised signals as alternatives by introducing additional datasets on image manipulation detection and formulating two classification tasks as positive and unlabeled learning problems. Based on these ideas, we propose a novel MMD method, namely Harmfully Manipulated Images Matter in MMD (MANI-M$^3$D). Extensive experiments across three benchmark datasets can demonstrate that \baby can consistently improve the performance of any MMD baselines.

Main paper: 
 related works: 
 In this section, we briefly review and introduce the related literature on MMD, manipulation detection, and PU learning.In general, the primary goal of MMD is to automatically distinguish misinformation consisting of text-image pairs on social media. Most existing MMD methods concentrate on creating powerful multimodal models to grasp complex semantic information [34, 44, 48] . For example, BMR [52] refines and fuses multimodal feature using an improved mixture-of-experts network. In addition, several MMD arts suggest modeling the inconsistency between different modalities [8, 15] , and they present a hypothesis that the inconsistency between modalities is a non-trivial clue for detecting misinformation. Following this hypothesis, CAFE [8] proposes a variational approach to calculate the inconsistency between modalities and leverage it to guide the multimodal feature fusion. Our study aims to improve MMD models by specifying manipulation and its intent features. Despite the importance of the image manipulation in misinformation detection, few works have explored the role of manipulation features. Previous studies [1, 3, 4, 28, 33] have intuitively highlighted the significance of such features, but specific models have not been developed yet.With the rapid development and popularization of multimedia technology, it has become increasingly easy to manipulate various multimedia contents, e.g., images and videos, through copy-moving [9] , splicing [22] or inpainting [27] . To automatically control the misuse of these techniques, IMD has become an active topic in the information forensics community [10, 35] . Briefly, The primary goal of IMD is to determine if an image has been manipulated and localize the specific manipulated area, which is a challenging segmentation task. Most of the existing methods focus on designing powerful neural models to extract effective semantic features and capture subtle manipulation traces [30, 39, 57] . Apart from network structures, some works are also dedicated to designing more efficient training strategies. For example, some pre-training datasets are synthesized by manipulating real images to extend the tampered class [49, 50, 58] . Motivated by these arts, we design a hierarchical visual encoder as the basic manipulation teacher to obtain multi-view manipulation features, pre-train it with large-scale IMD datasets, and adapt the model to the MMD task with synthesized PU learning.PU learning is a unique learning paradigm that aims to learn a binary classifier by accessing only a part of labeled positive samples and several unlabeled samples. Generally, existing PU learning methods are summarized into two basic categories, sampleselection and cost-sensitive methods. The sample-selection line leverages several heuristic strategies to select potential negative samples from the unlabeled data, and then train the binary classifier with the supervised or semi-supervised learning paradigm [20, 53] . For example, PULUS [29] learns a negative sample selector with reinforcement learning by the reward from the performance across the validation set. On the other hand, the cost-sensitive line designs a variety of empirical risks on negative samples and constrains them unbiased [6, 16, 25, 56] . For example, uPU [16] early proposes to unbiasedly estimate risks. Additionally, some current methods attempt to assign reliable pseudo labels to unlabeled samples [19, 29, 45] and design effective augmentation methods [26, 47] .In the MD community, some recent works also solve the PU misinformation detection topic [11, 43] . They present a weaklysupervised task, which learns misinformation detectors with partial real articles as the positive samples and regards the other articles as unlabeled samples. Unlike them, our Hami-m 3 d employs PU learning as an important tool to adapt the pre-trained IMD model to MMD datasets and resolve the issue of unknown intention labels. 
proposed hami-m d method: 
 In this section, we will introduce the proposed MMD model Hamim 3 d in more detail.𝑁 training samples D = {(x 𝑇 𝑖 , x 𝐼 𝑖 , 𝑦 𝑖 )} 𝑁 𝑖=1, where x 𝑇 and x 𝐼 are respectively the text content and images of an article, and 𝑦 ∈ {0, 1} denotes the corresponding veracity label (0/1 indicates real/fake). The basic target of MMD is to learn a detector to predict the veracity of any unseen article. The basic framework of the existing MMD method typically consists of three modules: feature encoder, feature fusion network, and predictor. The feature encoder extracts the unimodal semantic features of x 𝑇 𝑖 and x 𝐼 𝑖 . The feature fusion network integrates these features into a multimodal feature before feeding into the predictor module.We are inspired by the joint observation and assumption that fake articles may be highly relevant to harmfully manipulated images. Accordingly, for each sample, we can extract its latent manipulation feature and harmful feature and then fuse them with the semantic features to achieve more discriminative fused features. To estimate the two latent features, we conduct the manipulation classification and harmful classification as auxiliary tasks. Upon these ideas, we design Hami-m 3 d under a multi-task learning framework jointly learning the primary task of veracity classification with the two auxiliary tasks. To be specific, Hami-m 3 d consists of three primary modules: feature encoders module, feature fusion module, and predictors module. For clarity, the overall framework of Hamim 3 d is illustrated in Fig. 2 . In the following, we introduce these modules in more detail.Feature encoders module. This module consists of four specific feature encoders, including text encoder, image encoder, manipulation encoder, and intention encoder.Given a pair of text content x 𝑇 𝑖 and an image x 𝐼 𝑖 , the text encoder and image encoder extract their respective text and image features e 𝑇 𝑖 and e 𝐼 𝑖 . Specifically, we derive text and image features e 𝑇 𝑖 = F 𝚯 𝑇 (x 𝑇 𝑖 ) and e 𝐼 𝑖 = F 𝚯 𝐼 (x 𝐼 𝑖 ), respectively, by leveraging a pre-trained BERT model [12] and a ResNet34 model [17] . These features are then aligned into a shared feature space using two feedforward neural networks. Next, we directly input the image feature e 𝐼 𝑖 into a manipulation encoder to generate the manipulation feature e 𝑀 𝑖 = F 𝚯 𝑀 (e 𝐼 𝑖 ). Based on this manipulation feature, we then integrate it with the text and image features e 𝑇 𝑖 and e 𝐼 𝑖 into an intention encoder to obtain the intention feature e 𝐸 𝑖 = F 𝚯 𝐸 (e 𝑇 𝑖 , e 𝐼 𝑖 , e 𝑀 𝑖 ). Feature fusion module. Given these extracted features, the feature fusion module utilizes a multi-head attention network to integrate them into one fused feature z 𝑖 = F 𝚿 𝐹 (e 𝑇 𝑖 , e 𝐼 𝑖 , e 𝑀 𝑖 , e 𝐸 𝑖 ). Predictors module. This module contains three predictors trained on three different tasks: veracity classification, manipulation classification, and intention classification. Utilizing the fused feature z 𝑖 , a linear veracity classifier is employed to predict the veracity label as 𝑝 𝑖 = W 𝑉 z 𝑖 . The objective for the veracity classification task across D can be formulated as follows:EQUATIONwhere ℓ 𝐶𝐸 (•, •) denotes a cross-entropy loss function. Then, given manipulation and intention features e 𝑀 𝑖 and e 𝐸 𝑖 , we deploy manipulation and intention classifiers to generate their corresponding predictions𝑝 𝑀 𝑖 = W 𝑀 e 𝑀 𝑖 ∈ [0, 1] and 𝑝 𝐸 𝑖 = W 𝐸 e 𝐸 𝑖 ∈ [0, 1], where 𝑝 𝑀 𝑖 = 1 or 0 indicates whether the image has been manipulated or not, and 𝑝 𝐸 𝑖 = 1 or 0 represents the harmless or harmful intention behind the manipulation, respectively.Unfortunately, the ground-truth manipulation and intention labels are unknown in MMD datasets. To overcome this issue, we introduce two weakly supervised signals as substitutes to achieve the manipulation classification and intention classification tasks. Specifically, for manipulation classification, we train a manipulation teacher 𝑓 𝚷 (•) using additional IMD datasets, e.g., CASIAv2 [14] , with an objective L 𝑃𝑅𝐸 . To address the distribution shift issue between IMD datasets and MMD datasets, we then adapt the teacher by utilizing a PU loss L 𝑃𝑈 . Given the teacher's output 𝑦 𝑀 𝑖 = 𝑓 𝚷 (x 𝐼 𝑖 ), we can distill it to the prediction 𝑝 𝑀 𝑖 as follows:EQUATIONwhere 𝐷 𝐾𝐿 (• , •) denotes the Kullback-Leibler divergence function.For intention classification, we draw inspiration from the fact that if the image of the real article is manipulated, its intention must be harmless, and reformulate the intention classification into a PU learning problem, which can be specified by an objective L 𝐼𝑅 . Additionally, based on another fact that if the image of one article is manipulated with a harmful intention, the veracity label of this article must be fake, we can also check the reliability of 𝑝 𝐸 𝑖 , and filter out unreliable samples during training.Based on the aforementioned tasks, our overall objectives are as follows:EQUATIONEQUATIONwhere 𝛼, 𝛽, and 𝛿 are trade-off hyper-parameters to balance multiple loss functions. We will alternatively optimize our MMD model and the teacher model with the objectives in Eqs. ( 3) and (4). For clarity, the overall training pipeline is described in Alg. Figure 2 : The overall framework of Hami-m 3 d. Given text content x 𝑇 𝑖 and an image x 𝐼 𝑖 , we use four encoders including text encoder, image encoder, manipulation encoder, and intention encoder to extract their corresponding features. These features are then input into a feature fusion network to obtain a fused feature. Finally, we propose three predictors to achieve three different tasks: veracity classification, manipulation classification, and intention classification. sections, we introduce the manipulation and intention classification tasks in more detail.Generally, the manipulation classification task involves training a manipulation teacher 𝑓 𝚷 (•) and then distilling its predictions to the prediction 𝑞 𝑀 𝑖 with Eq. (2). To be specific, the optimization of the teacher model requires two objectives: a pre-training objective L 𝑃𝑅𝐸 and an objective L 𝑃𝑈 to adapt the model.First, we introduce a benchmark IMD dataset, denoted asD 𝜇 = {x 𝜇 𝑗 , 𝑦 𝜇 𝑗 } 𝑁 𝜇𝑗=1 , e.g., CASIAv2 [14] . The dataset consists of 𝑁 𝜇 pairs of images x 𝜇 and their corresponding ground-truth manipulation labels 𝑦 𝜇 ∈ {0, 1}, where 𝑦 𝜇 = 1 or 0 indicates x 𝜇 is manipulated or not. Given x 𝜇 𝑗 , we feed it into a ResNet18 as the backbone of our teacher model. Since recent some IMD arts suggest that the detection of manipulation trace requires not only semantic information but also subtle clues in images [13, 39] . Therefore, we follow Chen et al. [7] to extract the features of the inner layers of ResNet18, integrate them with a self-attention network, and predict the manipulation label of x 𝜇 𝑗 . Its objective can be formulated as follows:EQUATIONThen, due to the inevitable distribution shift problem between the IMD dataset D 𝜇 and the MMD dataset D, we propose to adapt the teacher model pre-trained across D 𝜇 to the MMD dataset through a PU learning framework. Specifically, given an image x 𝐼 𝑖 sampled from D, we randomly manipulate it with the copymoving approach [9] , and synthesize its manipulated version x 𝐼 𝑖 . Therefore, the manipulation label of x 𝐼 𝑖 can be naturally assigned as "manipulated", denoted as 𝑦 𝑀 𝑖 = 1, and form a training subsetP 𝑀 = { x 𝐼 𝑖 , 𝑦 𝑀 𝑖 = 1} 𝑁 𝑖=1 .Meanwhile, the manipulation label of x 𝐼 𝑖 is still unknown, so we form another unlabeled subset U 𝑀 = {x 𝐼 𝑖 } 𝑁 𝑖=1 . Accordingly, drawing inspiration from PU learning, which aims to train a binary classifier with partially labeled positive samples and sufficient unlabeled samples, we reformulate the manipulation classification across P 𝑀 ∪ U 𝑀 as a PU learning problem.Formally, several arts specify PU learning with various risk estimation approaches. In this work, we implement it with a variational PU learning framework [5] . 1 Given two subsets P 𝑀 ∼ P 𝑃 ≜ P(x 𝐼 |𝑦 𝑀 = 1) and U 𝑀 ∼ P 𝑈 ≜ P(x 𝐼 ),2 using a Bayes rule, we can estimate P 𝑃 as follows:EQUATIONwhere P 𝚷 is the data distribution with the parametric model 𝚷, and 𝑓 𝚷 ★ (•) represents an optimal teacher model. Accordingly, to optimize 𝑓 𝚷 (•) towards the optimal 𝑓 𝚷 ★ (•), prior studies [5] prove that we can minimize the KL divergence between P 𝑃 and P 𝚷 , which Manipulate images in B and form a manipulated B.Calculate L 𝑃𝑅𝐸 with B 𝜇 and L 𝑃𝑈 with B ∪ B.Optimize 𝚷 with Eq. ( 4).Calculate L 𝑉 𝐶 , L 𝐾𝐷 , and L 𝐼𝑅 with B.Optimize 𝚯 with Eq. (3). 10: end for is formalized as follows:EQUATION= E P 𝑃 log 𝑓 𝚷 ★ (x 𝐼 ) + E P 𝑃 log P 𝑃 (x 𝐼 ) -log E P 𝑈 𝑓 𝚷 ★ (x 𝐼 ) -E P 𝑃 log 𝑓 𝚷 (x 𝐼 ) + E P 𝑃 log P 𝑃 (x 𝐼 ) -log E P 𝑈 𝑓 𝚷 (x 𝐼 ) .Accordingly, the PU optimization objective can be specified as follows:EQUATIONBy optimizing these two objectives L 𝑃𝑅𝐸 and L 𝑃𝑈 with Eq. ( 4), we can obtain a strong manipulation teacher, and distill the prediction 𝑦 𝑀 𝑖 from the teacher to 𝑝 𝑀 𝑖 with Eq. ( 2) [18, 24, 59 ]. Note that during the optimization process, we first use L 𝑃𝑅𝐸 to warm up the teacher model for 10 epochs to prevent cold start problems in the optimization of L 𝑃𝑈 .Given the intention feature e 𝐸 𝑖 , the intention classification task aims to make it discriminative about the intention behind the image manipulation. To solve the problem of unknown intention labels, we provide two facts as weakly supervised signals to supervise the intention prediction 𝑝 𝐸 𝑖 . Specifically, the first fact is presented as: Fact 1. If the image of the real article is manipulated, its intention must be harmless; But if the image of the fake article is manipulated, its intention may be harmful or harmless. Written as:𝑦 𝐸 𝑖 = 1, 𝑦 𝑖 = 0 ∧ 𝑦 𝑀 𝑖 = 1, 0 or 1, 𝑦 𝑖 = 1 ∧ 𝑦 𝑀 𝑖 = 1, where 𝑦 𝐸 𝑖 denotes the intention label of the 𝑖-th sample. Based on this fact, we can form a subset D 𝐸 ∈ D where all samples satisfy 𝑦 𝑀 = 1, and then split D 𝐸 into a positive subset P 𝐸 where 𝑦 = 0 and an unlabeled subset U 𝐸 where 𝑦 = 1. Accordingly, the intention classification across P 𝐸 ∪U 𝐸 can also be reformulated as a PU learning problem. Similar to the formula in Eq. ( 8), its objective is represented as: In addition, another fact is presented as:EQUATIONFact 2.If the image of one article is manipulated by a harmful intention, the veracity label of this article must be fake; But if the image of one article is manipulated by a harmless intention, the veracity label of this article may be real or fake. Written as:𝑦 𝑖 = 1, 𝑦 𝐸 𝑖 = 1 ∧ 𝑦 𝑀 𝑖 = 1, 0 or 1, 𝑦 𝐸 𝑖 = 0 ∧ 𝑦 𝑀 𝑖 = 1.This fact can be regarded as a metric to check the reliability of predictions 𝑝 𝑀 𝑖 and 𝑝 𝐸 𝑖 . For a sample that satisfies 𝑝 𝑀 𝑖 = 1 and 𝑝 𝐸 𝑖 = 1, if its ground-truth veracity label 𝑦 𝑖 ≠ 1, at least one of its predictions 𝑝 𝑀 𝑖 and 𝑝 𝐸 𝑖 is incorrect, and we remove these incorrect samples when optimizing the model with Eq. ( 3). 
experiments: 
 In this section, we conduct extensive experiments and compare Hami-m 3 d to existing MMD baselines to evaluate its performance.Datasets. To evaluate the empirical performance of Hami-m 3 d, we conduct our experiments across three prevalent MMD datasets GossipCop [37] , Weibo [23] , and Twitter [1, 2] , their statistics are shown in Table 1 . Specifically, GossipCop and Weibo consist of 12,840 and 9,528 text-image pairs, respectively, and their text and images typically have a one-to-one correspondence. Differently, the Twitter dataset contains 13,924 texts but only 514 images, so it exhibits a complex one-text-to-many-images or one-image-to-many-texts correspondence, which is a more challenging scenario. We follow previous works [46, 54] to process and split these datasets into training, validation, and test subsets by a ratio of 7:1:2.Baselines. We compare five MMD baselines and their improved versions using Hami-m 3 d in our experiments. These baselines are briefly introduced as follows:• Basic model extracts text and image features with a pretrained BERT model [12] and a ResNet34 [17] , respectively. Then, we use typical FFNN layers to map and align two features into a shared space, and then fuse multimodal features and predict veracity labels with MLP layers. • SAFE [60] designs a similarity-aware multimodal fusion module for MMD.• MCAN [51] proposes a multimodal co-attention network to fuse multimodal features effectively and considers intermodality correlations.• CAFE [8] guides the multimodal feature fusion by assigning adaptive weights with a variational method, and aligns unimodal features with a contrastive learning regularization. • BMR [52] creates an elaborate network with improved mixtureof-experts to extract and fuse multimodal features.The results of all baselines are re-produced by us to use the BERT model and ResNet34 as the feature extractors.Implementation Details. To preprocess the raw data, we resize and randomly crop raw images to 224 × 224, and truncate text content to 128 word tokens. Then, we employ pre-trained ResNet34 and BERT 3 to capture visual and text features, and the first 9 Transformer layers of the BERT model are frozen. For the manipulation teacher, we use a shallow ResNet18 model as its backbone, and we pre-train it with an existing benchmark dataset CASIAv24 [14, 31] on image manipulation detection, which consists of 12,614 images, including 7,491 authentic and 5,123 tampered images. During training, we fine-tune the BERT model using an Adam optimizer with a learning rate of 3 × 10 -5 and optimize the other modules using Adam with a learning rate of 10 -3 , and the batch size is consistently fixed to 32. We empirically set the hyperparameters 𝛼, 𝛽, 𝛿, and 𝐾 to 0.1, 0.1, 0.1, and 10, respectively. Meanwhile, to prevent overfitting, we stop the training early when no better Macro F1 score appears for 10 epochs.We compare the performance of our model Hami-m 3 d against 5 baseline models across 3 benchmark datasets, and evaluate them using 8 typical metrics. The experimental results are reported in Table 2 . Generally, Table 2 reports the scores of Avg. Δ, which represents the average improvements of Hami-m 3 d over the baseline models across all evaluation metrics. We observe significant improvements of Hami-m 3 d over all these baselines. For instance, on the Weibo dataset, Hami-m 3 d improves the BMR model by approximately 1.79, and on the Twitter dataset, it improves the basic model by 1.84. Observing the performance of Hami-m 3 d in detail on different metrics, our model consistently outperforms the baseline models on all evaluation metrics. For example, on the Gossipcop dataset, it outperforms the BMR model by approximately 2.57 in the fake class recall score. These results demonstrate the effectiveness of our approach and highlight the role of manipulation and intention features in detecting misinformation. Additionally, we observe that the order of improvements induced by Hami-m 3 d across the three datasets is roughly ranked as Twitter > Weibo > Gossip-Cop. This phenomenon suggests that, firstly, for smaller datasets, Hami-m 3 d compensates for the lack of semantic information by exploiting manipulation and intention features, leading to larger improvements. Secondly, we observe that there are more manipulated images in the Twitter dataset, allowing our manipulation features to have a greater impact, which indirectly demonstrates the accuracy of our extracted manipulation features.To evaluate the effectiveness of all objective functions and features in Hami-m 3 d, we conduct an ablative experiment on an English dataset GossipCop, and a Chinese dataset Weibo, their experimental results are shown in Tables 3 and 4 . The descriptions of these ablative versions are as follows:• • w/o L 𝐾𝐷 , L 𝐼𝑅 represents not using any objective function to supervise the manipulation and intention features, and the optimization of these two features solely relies on the cross-entropy loss L 𝐶𝐸 of veracity prediction. Due to the strong dependency of L 𝐼𝑅 on L 𝐾𝐷 , we exclude both of them in this analysis; • w/o e 𝑀 , w/o e 𝐸 , and w/o e 𝑀 , e 𝐸 represents the removal of e 𝑀 and e 𝐸 and their related training losses, as well as the removal of both features, equivalent to the baseline model without using the method proposed in this paper.In general, removing each module leads to a decrease in the prediction results of Hami-m 3 d, confirming their effectiveness. Specifically, comparing the ablation results of the three objectives, their performance is roughly ranked as w/o L 𝑃𝑈 > w/o L 𝑃𝑅𝐸 > w/o L 𝐾𝐷 , L 𝐼𝑅 , and the removal of L 𝑃𝑅𝐸 and L 𝐾𝐷 , L 𝐼𝑅 has the greatest impact on the results of our model, with several results even falling below the baseline model. L 𝑃𝑅𝐸 effectively guides the manipulation teacher to accurately determine whether an image has been manipulated. Without L 𝑃𝑅𝐸 , the teacher's prediction performance deteriorates, indirectly leading to more confused features obtained by the manipulation encoder, thereby significantly affecting the veracity prediction results. On the other hand, L 𝐾𝐷 and L 𝐼𝑅 impose no constraints on the manipulation and intention features, not only increasing the computational burden of the baseline model but also introducing meaningless and non-discriminative features, directly affecting the discriminative ability of the final veracity feature e. Then, comparing the ablation results of the three features, their performance is ranked as w/o e 𝐸 > w/o e 𝑀 > w/o e 𝑀 , e 𝐸 , indicating that both features contribute to enhancing the discriminative ability of the final multimodal feature, and e 𝑀 has a greater impact.In Hami-m 3 d, 𝛼 and 𝛽 are crucial hyper-parameters, which represent the weights of L 𝐾𝐷 and L 𝐼𝑅 to balance training among multiple losses. Therefore, in this section, we conduct sensitivity experiments on these two hyper-parameters to analyze whether our model is sensitive to these parameters and to provide evidence for the selection of hyper-parameters in Hami-m 3 d. The specific experimental results are shown in Fig. 3 . We conduct experiments across both English and Chinese datasets, GossipCop and Weibo, respectively, and report the Macro F1 metric in Fig. 3 . 𝛼 and 𝛽 are selected from the set {0, 0.01, 0.1, 1, 10}, where 𝛼 or 𝛽 = 0 indicates that the corresponding objective function does not need to be trained. The experimental results show that the model is quite sensitive to both hyper-parameters and consistently has the best performance when 𝛼 = 0.1 and 𝛽 = 0.1. As they increase or decrease, the model's performance shows a decreasing trend. Therefore, in implementing all the experiments in this paper, we always choose 𝛼 = 0.1 and 𝛽 = 0.1. When 𝛼 and 𝛽 are small, the manipulation and intention features are not sufficiently trained, leading to insufficient discriminative features that degrade the model's prediction results, even below the baseline model. Conversely, when they are large, the model's optimization tends to favor their corresponding losses, reducing the weight of the veracity prediction objective L 𝐶𝐸 and degrading the veracity prediction results.To analyze the discriminative nature of the extracted manipulation and intention features, we visualize these features, as shown in Fig. 4 . Specifically, we choose the Weibo dataset for visualization analysis, using the T-SNE method [41] to reduce the dimensionality of the multimodal feature z, manipulation feature e 𝑀 , and intention feature e 𝐸 to 2D, and displaying the corresponding 2D points in Fig. 4 . Fig. 4 (a) illustrates the visualization of the multimodal features of the basic model, while Fig. 4(b) shows the visualization of the multimodal features with the addition of our proposed Hamim 3 d. By comparing the two results, we can observe that our method can separate the two clusters of real and fake classes from each other, thereby improving the discriminative nature of the multimodal features to a certain extent. Fig. 4 (c) displays the visualization results of the manipulation feature e 𝑀 , where we use the results provided by the teacher model to distinguish between manipulated and unmanipulated images. In the result, we can observe that the manipulation feature has strong discriminative power in determining whether an image has been manipulated, demonstrating the No idea if \"Suspect 2\" is missing Brown student Sunil Tripathi but looks more than a little like him: effectiveness of knowledge distillation in inheriting the discriminative ability of the manipulation teacher to the manipulation encoder. Fig. 4 (d) visualizes only the intention features e 𝐸 of samples classified as "manipulated" in the manipulation classification task. We find that the intention features of real and fake samples are clearly separated. However, some fake samples are mixed in the real cluster, reflecting the fact that fake samples may also have a harmless intent.We provide three representative examples for illustrating the performance of our classifiers on three tasks in Fig. 5 . The first example involves an image that has been manipulated in a harmful intention. Our model confidently predicts both the manipulation and the intention, accurately identifying its veracity label as fake; The second example presents an image with manipulated colors. Although our model correctly identifies the manipulation and provides accurate veracity predictions, it assigns a low-confidence probability to this type of manipulation, suggesting that there is room for improvement in detecting certain manipulation techniques; The third example illustrates an image that has been harmlessly manipulated, merely slicing it without any harmful intention. Our model also provides accurate predictions for all three tasks. In summary, our model's performance across the three tasks is commendable, but it is susceptible to subtle manipulations that require further improvement. 
conclusion: 
 In this paper, we aim to identify multimodal misinformation by recognizing manipulation traces of images in articles, as well as understanding the underlying intention behind such manipulation. To this end, we introduce a novel MMD model named Hami-m 3 d, which extracts manipulation and intention features and incorporates them into the overall multimodal features. To make manipulation and intention features discriminative towards whether the image has been harmfully manipulated, we propose two classifiers and predict their respective labels. To address unknown manipulation and intention labels, we propose two weakly supervised signals by learning a manipulation teacher with additional IMD datasets and using two PU learning objectives to adapt and supervise the classifier. Our experimental results can demonstrate that Hami-m 3 d can significantly improve the performance of its baseline models.  
: 
 The statistics on an MMD dataset Twitter illustrate the quantitative relationship between image manipulation and veracity labels. We use a pre-trained image manipulation detector to discriminate whether the image has been manipulated. We also provide several examples of images manipulated with harmful and harmless intentions.spread widely with malicious intentions, causing damages to people's mental and property [40, 42] . To eliminate such damages, the primary task becomes to automatically detect misinformation from social media, giving birth to the active topic named Misinformation Detection (MD). Generally, the objective of MD is to train the veracity predictor that can automatically distinguish whether an article is real or fake. The previous arts map raw articles into a high-dimensional semantic space and learn potential correlations between these semantics and their veracity labels by designing a variety of deep models [21, 36, 55, 61] . However, most existing MD efforts solely handle text-only articles, which is unrealistic for nowadays social media platforms that contain a large amount of multimodal content. Therefore, Multimodal Misinformation Detection (MMD) approaches have been developed recently to meet the practical need, which detects misinformation containing multiple modalities, e.g., text and image. The typical MMD pipelines first capture unimodal semantic features with various prevalent feature extractors [12, 17] , and then align and fuse them into a multimodal feature to predict the veracity labels [8, 15, 34, 44, 48] . Building upon this pipeline, cutting-edge MMD works design innovative multimodal interaction strategies to fuse semantic features [23, 34, 52] , and learn the semantic inconsistency between different modalities [8, 32, 38] .The existing MMD methods can be beneficial from modality features, however, they also treat MMD as a standard classification problem that relies on the semantic information of samples. In contrast, misinformation is a complex phenomenon whose veracity is influenced by various aspects. As surveyed in [3, 4] , a majority of fake articles may contain manipulated images created by various techniques, e.g., copy-moving and splicing [9, 22] . To verify this viewpoint, we conduct a preliminary statistical analysis across a public MMD dataset Twitter, as shown in Fig. 1 . The observation is two-fold. On one hand, we observe that approximately 66.4% of fake articles involve manipulated images, motivating us that manipulated images can be a discriminative indicator for fake articles. On the other hand, we observe that approximately 10.0% of real articles also involve manipulated images, seeming a bit conflict to the commonsense that real articles should be completely real. We examined all those manipulated images and empirically found the ones of fake articles are more likely with harmful intentions such as deception and pranks, but the ones of real articles are with harmless intentions such as watermarking and aesthetic enhancements, as examples shown in Fig. 1 . Upon these observations and the intention perspective [10] , we assume that harmfully manipulated images can be a discriminative indicator for fake articles.Motivated by these considerations, we propose to detect misinformation by extracting distinctive manipulation features that reveal whether the image is manipulated, as well as intent features that differentiate between harmful and harmless intentions behind the manipulation. Accordingly, we design a novel MMD framework, namely HArmfully Manipulated Images Matter in MMD (Hami-m 3 d). Specifically, we extract manipulation and intention features from multimodal articles and use them to formulate two binary manipulation and intention classification tasks. We then supervise these two classifiers with their corresponding binary labels. Unfortunately, their ground-truth labels are unknown in MMD datasets. To address this issue, we suggest the following two weakly supervised signals as substitutes for these labels. First, to supervise the manipulation classifier, we resort to a knowledge distillation paradigm [18, 24, 59] to train a manipulation teacher, which can discriminate whether an image has been manipulated, and distill its discriminative capabilities to the manipulation classifier. Specifically, we introduce additional benchmark datasets on Image Manipulation Detection (IMD) [14] to pre-train the manipulation teacher. To further alleviate the distribution shift problem between IMD data and MMD data, we synthesize some manipulated images based on MMD datasets and formulate a Positive and Unlabeled (PU) learning objective to transfer the teacher to the MD data. Second, based on the fact that if the image of the real information has been manipulated, its intention must be harmless, we can also formulate intention classification as a PU learning problem, and solve it by a variational PU method.We evaluate our method Hami-m 3 d across 3 benchmark MMD datasets and compare it with 5 baseline MMD models. The experimental results demonstrate that Hami-m 3 d can improve the average performance of its baselines by approximately 1.21 across all metrics, which indicates the effectiveness of Hami-m 3 d. Our source code and data will be released once the paper is accepted.In summary, our contributions are following three-folds:• We suggest that image manipulation and its underlying intentions matter in MMD. • Extensive experiments are conducted across three MD datasets to demonstrate the improvements of Hami-m 3 d on the existing baseline model. 
 
 Appendix: The variational PU learning follows a "selected completely at random" assumption that does not require additional class priors, which is consistent with the scenario of our paper. Additionally, it has empirically demonstrated superior performance in Hami-m 3 d.Since P( x 𝐼 ) and P(x 𝐼 ) are independently and identically distributed, to keep our notations clear, we uniformly utilize x 𝐼 and 𝑦 𝑀 to indicate the image and its manipulation label.Downloaded from https://huggingface.co/bert-base-uncased.Downloaded from https://github.com/SunnyHaze/CASIA2.0-Corrected-Groundtruth. 


Decision: This paper considers the multi-modal misinformation detection task. The authors give a special focus on harmfully manipulated images and propose to detect fake news by learning manipulation features that indicate whether the image has been manipulated. All reviewers provide positive final ratings, considering that this paper provides a new perspective and a reasonable technical solution. I recommend the acceptance of this paper, but the authors are strongly required to revise this paper carefully according to the reviews to further improve the paper's quality.

Reviews: review: paper_summary: paper_summary: Existing multimodal misinformation detection(MMD) methods often concentrate on the semantic associations and inconsistencies between multimodal contents, but they tend to neglect the potential clues found in traces of image manipulation. The fact that an image has been manipulated and the underlying intentions of such manipulation, whether harmful or harmless, are of paramount importance for detecting misinformation. The paper introduces a novel approach known as Harmfully Manipulated Images Matter in MMD (Hami-m3d), designed to detect misinformation on social media platforms that contains manipulated images within multimodal content.
The method begins by extracting semantic features from textual and visual content through feature encoders, followed by the utilization of a manipulation encoder and an intention encoder to separately derive features that indicate the presence of image manipulation and the harmfulness of the intentions behind it. Given that direct labels for manipulation and intentions are typically unavailable, Hami-m3d leverages weakly supervised signals, employing knowledge distillation to train a manipulation teacher model, and integrates a Positive and Unlabeled (PU) learning framework to adapt and supervise the classifiers. Operating under a multi-task learning framework, Hami-m3d concurrently refines the primary task of veracity classification along with auxiliary tasks of manipulation and intention classification, leading to a marked improvement in the performance of baseline models across various benchmark datasets.
Comprehensive experiments conducted across three benchmark datasets have demonstrated that Hami-m3d can consistently elevate the efficacy of existing MMD baseline models.
strength_weakness: strength_weakness: This article proposes a method called Hami-m3d, which introduces a new dimension to multimodal misinformation detection by combining manipulation features and intention features to detect image-manipulated misinformation.
Utilizing weakly supervised learning and knowledge distillation techniques, it addresses the issue of missing labels, offering a comprehensive solution to enhance the accuracy of misinformation detection.
limitations: limitations: The introduction of the Hami-m3d method is not clear. The model distillation part requires a more detailed explanation. 
The experiments only compared with a relatively new fake news detection method BMR and some older methods, and did not compare with other methods from recent years, which makes the comparison scheme less convincing. 
There are several blank lines at 164-166, 454-457, and the layout here can be improved. Figure 2, the core framework diagram, can be more balanced and needs to be drawn more beautifully.

This paper incorporates manipulation intent into the MMD task and enhances model performance using weakly supervised signals and knowledge distillation, which has a certain degree of innovation. However, there are some shortcomings in the overall layout and drawing that need improvement. In addition, the experiments should include not only comparisons with classic models but also with the latest models.
paper_summary: paper_summary: This is an article about multimodal misinformation detection. The authors start from manipulated traces of the images based on intuition and statistical analysis to uncover image manipulation and detect misinformation. This approach aligns with intuitive thinking and is supported by statistics, making it a reasonable and feasible method for multimodal misinformation detection.
strength_weakness: strength_weakness: 1.The intuitive thinking behind the method is not particularly novel, but it is very reasonable. It aligns with intuitive cognition and is supported by statistical information. Figure 1 illustrates the motivation for this method very clearly and intuitively.

2.The Hami-m3d framework is clear and integrates three classification sub-tasks. Notably, it is not an independent network and can be combined with current multimodal misinformation detection networks to further enhance performance. This is clearly demonstrated in the results shown in Table 2.

3.The experimental results in the paper are comprehensive and validate the proposed method. Table 2.
limitations: limitations: 1.Although manipulation detection based on statistical intuition is effective and reasonable for identifying virtual information, it does not seem particularly unique. Some tasks might also use similar techniques for tampering detection and face modification/generation detection to enhance their results.

2."The image of the real information has been manipulated, its intention must be harmless", is such manipulation for purposes like beautification? Based on this idea, does the design of this method rely entirely on the introduced IMD dataset for training the teacher model?
The most critical aspects of manipulation detection and intent recognition heavily rely on the teacher model trained on the IMD dataset. It appears there are no additional special designs beyond this.

3. Some manipulations seem to easily mislead the model into categorizing real images entirely as misinformation. It seems more reasonable to add some noise layers to the teacher model.
paper_summary: paper_summary: This paper focuses on the task of multimodal misinformation detection and introduces a novel approach by incorporating manipulation and its intent as key components of this task. This paper proposes the HAMI-M^3D model, which includes manipulation and intent classification as auxiliary tasks. These are treated as positive and unlabeled (PU) learning problems. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method.
strength_weakness: strength_weakness: 1. The paper presents an intriguing research perspective in the context of MMD-related studies. The relationship between veracity labels, manipulation labels, and intention labels is intuitively complex. This article innovatively models these as a problem of positive and unlabeled (PU) learning, offering valuable insights that could inspire further research in the field.
2. The experiments are extensive, testing the proposed model against five baselines across three datasets. The authors also conduct a detailed analysis of parameter sensitivity and provide interesting visualization, enhancing the understanding of the model’s dynamics and its performance.
limitations: limitations: **Motivation:**
- Despite the significant link between image manipulation and the authenticity of news, image tampering in most real-world scenarios and MMD datasets is relatively rare. I have reservations about the claim in the paper that "approximately 66.4% of fake articles involve manipulated images" observed in the MediaEval15 dataset. This figure may not be representative and could mislead readers. I recommend that the authors conduct a prior analysis of other datasets or revise this claim to prevent potential misguidance.

**Experiments:**
- The proposed method only improves the average performance of its baselines by approximately 1.21% across all metrics, which I’m afraid may not be considered effective given the variability inherent in deep learning.
- The paper mentions that the GossipCop dataset comprises 12,840 items, which differs from the original count of 18,417. Clarification is needed on how this number was derived.
- The ratio of fake to real news in the GossipCop dataset is close to 1:10. How was this imbalance addressed? Was this ratio maintained across train, validation, and test splits?
 - What settings were used to repeat experiments to obtain the reported means and variances?
 - In the case study, why were the second and third examples both interpreted as harmless tampering, yet one was classified as real and the other as fake?

**Other Necessary Revisions:**
- Ensure correct citations, including referencing the Twitter dataset upon first mention; also, the part of the GossipCop dataset mentioned is actually from the FakeNewsNet dataset and should be referred to as such.
- It is advisable to label intent as 0/1 to clarify which class they correspond to, avoiding confusion.
- The sub-title in Figure 4(b), "MANI-MMD," should be corrected to "HAMI-M^3D."
paper_summary: paper_summary: The authors propose to detect misinformation by learning manipulation features that indicate whether the image has been manipulated, as well as intention features regarding the harmful and harmless intentions of the manipulation and introduce the model Harmfully Manipulated Images Matter ($HAMI-M^3D$). To address the challenge of unknown manipulation and intention labels,  $HAMI-M^3D$ employs two weakly supervised signals by learning a manipulation teacher with additional Image Manipulation Detection datasets and using two Positive-Unlabeled (PU) learning objectives to adapt and supervise the classifier.
strength_weakness: strength_weakness: 1. The authors creatively tackle the issue of unavailable manipulation and intention labels. This innovative attempt is noteworthy and could inspire further research in the field.
2. The paper is well-organized and adheres to academic writing standards. The structure is complete, making the research easy to follow and understand.
3. The authors conducted extensive experiments across three misinformation detection datasets to validate the performance of the proposed model.
limitations: limitations: 1. **Label Definition Ambiguity:** There seems to be a contradiction in the definition of labels (indicating 1 or 0). In lines 510-512, $y_i^E=1$ indicates harmless, while in lines 537-539, $y_i^E=1$ indicates harmful. Clarifying this ambiguity is essential for better understanding.
2. **About Error Propagation:** There is a potential issue where errors in the manipulation detection part might propagate to the intention detection part. How do the authors address this problem?
3. **Experimental Results:** The improvements in experimental results are not very significant, including those from ablation studies. This could be due to the relatively small ratio of image manipulation data in the datasets used. The authors are encouraged to test their model on datasets with more manipulated images to validate its efficacy further.
4. **PU Learning Outcomes:** Providing the final proportions of manipulation and intention labels in real and fake data (determined by the PU learning approach) would be useful for understanding the data condition.
5. **Typos:** There are several typos in the paper, such as $L_{VC}$ being referred to as $L_{VP}$ in Figure 2. Correcting these would enhance the manuscript's clarity.
6. **Code and Data Availability:** The authors have not provided their code and data. Making these resources available would significantly benefit the research community by enabling replication and further study.