Panagiotis C. Petrantonakis

Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos et al.

Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation (MM) and the urgent need for effective strategies to detect and prevent its spread. In recent years, the challenge of multimodal misinformation detection (MMD) has garnered significant attention by researchers and has mainly involved the creation of annotated, weakly annotated, or synthetically generated training datasets, along with the development of various deep learning MMD models. However, the problem of unimodal bias has been overlooked, where specific patterns and biases in MMD benchmarks can result in biased or unimodal models outperforming their multimodal counterparts on an inherently multimodal task; making it difficult to assess progress. In this study, we systematically investigate and identify the presence of unimodal bias in widely-used MMD benchmarks, namely VMU-Twitter and COSMOS. To address this issue, we introduce the "VERification of Image-TExt pairs" (VERITE) benchmark for MMD which incorporates real-world data, excludes "asymmetric multimodal misinformation" and utilizes "modality balancing". We conduct an extensive comparative study with a Transformer-based architecture that shows the ability of VERITE to effectively address unimodal bias, rendering it a robust evaluation framework for MMD. Furthermore, we introduce a new method -- termed Crossmodal HArd Synthetic MisAlignment (CHASMA) -- for generating realistic synthetic training data that preserve crossmodal relations between legitimate images and false human-written captions. By leveraging CHASMA in the training process, we observe consistent and notable improvements in predictive performance on VERITE; with a 9.2% increase in accuracy. We release our code at: https://github.com/stevejpapad/image-text-verification

Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos et al.

Online misinformation is often multimodal in nature, i.e., it is caused by misleading associations between texts and accompanying images. To support the fact-checking process, researchers have been recently developing automatic multimodal methods that gather and analyze external information, evidence, related to the image-text pairs under examination. However, prior works assumed all external information collected from the web to be relevant. In this study, we introduce a "Relevant Evidence Detection" (RED) module to discern whether each piece of evidence is relevant, to support or refute the claim. Specifically, we develop the "Relevant Evidence Detection Directed Transformer" (RED-DOT) and explore multiple architectural variants (e.g., single or dual-stage) and mechanisms (e.g., "guided attention"). Extensive ablation and comparative experiments demonstrate that RED-DOT achieves significant improvements over the state-of-the-art (SotA) on the VERITE benchmark by up to 33.7%. Furthermore, our evidence re-ranking and element-wise modality fusion led to RED-DOT surpassing the SotA on NewsCLIPings+ by up to 3% without the need for numerous evidence or multiple backbone encoders. We release our code at: https://github.com/stevejpapad/relevant-evidence-detection

Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos et al.

Out-of-context (OOC) misinformation poses a significant challenge in multimodal fact-checking, where images are paired with texts that misrepresent their original context to support false narratives. Recent research in evidence-based OOC detection has seen a trend towards increasingly complex architectures, incorporating Transformers, foundation models, and large language models. In this study, we introduce a simple yet robust baseline, which assesses MUltimodal SimilaritiEs (MUSE), specifically the similarity between image-text pairs and external image and text evidence. Our results demonstrate that MUSE, when used with conventional classifiers like Decision Tree, Random Forest, and Multilayer Perceptron, can compete with and even surpass the state-of-the-art on the NewsCLIPpings and VERITE datasets. Furthermore, integrating MUSE in our proposed "Attentive Intermediate Transformer Representations" (AITR) significantly improved performance, by 3.3% and 7.5% on NewsCLIPpings and VERITE, respectively. Nevertheless, the success of MUSE, relying on surface-level patterns and shortcuts, without examining factuality and logical inconsistencies, raises critical questions about how we define the task, construct datasets, collect external evidence and overall, how we assess progress in the field. We release our code at: https://github.com/stevejpapad/outcontext-misinfo-progress

Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos et al.

With the expansion of social media and the increasing dissemination of multimedia content, the spread of misinformation has become a major concern. This necessitates effective strategies for multimodal misinformation detection (MMD) that detect whether the combination of an image and its accompanying text could mislead or misinform. Due to the data-intensive nature of deep neural networks and the labor-intensive process of manual annotation, researchers have been exploring various methods for automatically generating synthetic multimodal misinformation - which we refer to as Synthetic Misinformers - in order to train MMD models. However, limited evaluation on real-world misinformation and a lack of comparisons with other Synthetic Misinformers makes difficult to assess progress in the field. To address this, we perform a comparative study on existing and new Synthetic Misinformers that involves (1) out-of-context (OOC) image-caption pairs, (2) cross-modal named entity inconsistency (NEI) as well as (3) hybrid approaches and we evaluate them against real-world misinformation; using the COSMOS benchmark. The comparative study showed that our proposed CLIP-based Named Entity Swapping can lead to MMD models that surpass other OOC and NEI Misinformers in terms of multimodal accuracy and that hybrid approaches can lead to even higher detection accuracy. Nevertheless, after alleviating information leakage from the COSMOS evaluation protocol, low Sensitivity scores indicate that the task is significantly more challenging than previous studies suggested. Finally, our findings showed that NEI-based Synthetic Misinformers tend to suffer from a unimodal bias, where text-only MMDs can outperform multimodal ones.

Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos et al.

Multimodal misinformation, such as miscaptioned images, where captions misrepresent an image's origin, context, or meaning, poses a growing challenge in the digital age. To support fact-checkers, researchers have focused on developing datasets and methods for multimodal misinformation detection (MMD). Due to the scarcity of large-scale annotated MMD datasets, recent approaches rely on synthetic training data created via out-of-context pairings or named entity manipulations (e.g., altering names, dates, or locations). However, these often yield simplistic examples that lack real-world complexity, limiting model robustness. Meanwhile, Large Vision-Language Models (LVLMs) remain underexplored for generating diverse and realistic synthetic data for MMD. To address, we introduce "Miscaption This!", a collection of LVLM-generated miscaptioned image datasets. Additionally, we introduce "Latent Multimodal Reconstruction" (LAMAR), a network trained to reconstruct the embeddings of truthful captions, providing a strong auxiliary signal to guide detection. We explore various training strategies (end-to-end vs. large-scale pre-training) and integration mechanisms (direct, mask, gate, and attention). Extensive experiments show that models trained on "MisCaption This!" generalize better to real-world misinformation while LAMAR achieves new state-of-the-art on both NewsCLIPpings and VERITE benchmarks; highlighting the value of LVLM-generated data and reconstruction-based networks for advancing MMD. Our code is available at https://github.com/stevejpapad/miscaptioned-image-reconstruction

Efthymia Amarantidou, Christos Koutlis, Symeon Papadopoulos et al.

The advent of accessible Generative AI tools enables anyone to create and spread synthetic images on social media, often with the intention to mislead, thus posing a significant threat to online information integrity. Most existing Synthetic Image Detection (SID) solutions struggle on generated images sourced from the Internet, as these are often altered by compression and other operations. To address this, our research enhances SID by exploring data augmentation combinations, leveraging a genetic algorithm for optimal augmentation selection, and introducing a dual-criteria optimization approach. These methods significantly improve model performance under real-world perturbations. Our findings provide valuable insights for developing detection models capable of identifying synthetic images across varying qualities and transformations, with the best-performing model achieving a mean average precision increase of +22.53% compared to models without augmentations. The implementation is available at github.com/efthimia145/sid-composite-data-augmentation.

Zacharias Chrysidis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos et al.

Automated fact-checking (AFC) is garnering increasing attention by researchers aiming to help fact-checkers combat the increasing spread of misinformation online. While many existing AFC methods incorporate external information from the Web to help examine the veracity of claims, they often overlook the importance of verifying the source and quality of collected "evidence". One overlooked challenge involves the reliance on "leaked evidence", information gathered directly from fact-checking websites and used to train AFC systems, resulting in an unrealistic setting for early misinformation detection. Similarly, the inclusion of information from unreliable sources can undermine the effectiveness of AFC systems. To address these challenges, we present a comprehensive approach to evidence verification and filtering. We create the "CREDible, Unreliable or LEaked" (CREDULE) dataset, which consists of 91,632 articles classified as Credible, Unreliable and Fact checked (Leaked). Additionally, we introduce the EVidence VERification Network (EVVER-Net), trained on CREDULE to detect leaked and unreliable evidence in both short and long texts. EVVER-Net can be used to filter evidence collected from the Web, thus enhancing the robustness of end-to-end AFC systems. We experiment with various language models and show that EVVER-Net can demonstrate impressive performance of up to 91.5% and 94.4% accuracy, while leveraging domain credibility scores along with short or long texts, respectively. Finally, we assess the evidence provided by widely-used fact-checking datasets including LIAR-PLUS, MOCHEG, FACTIFY, NewsCLIPpings+ and VERITE, some of which exhibit concerning rates of leaked and unreliable evidence.

Anastasios Skoularikis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos et al.

Recent advances in multimodal AI have enabled progress in detecting synthetic and out-of-context content. However, existing efforts largely overlook the intent behind AI-generated images. To fill this gap, we introduce S-HArM, a multimodal dataset for intent-aware classification, comprising 9,576 "in the wild" image-text pairs from Twitter/X and Reddit, labeled as Humor/Satire, Art, or Misinformation. Additionally, we explore three prompting strategies (image-guided, description-guided, and multimodally-guided) to construct a large-scale synthetic training dataset with Stable Diffusion. We conduct an extensive comparative study including modality fusion, contrastive learning, reconstruction networks, attention mechanisms, and large vision-language models. Our results show that models trained on image- and multimodally-guided data generalize better to "in the wild" content, due to preserved visual context. However, overall performance remains limited, highlighting the complexity of inferring intent and the need for specialized architectures.

Paschalis Giakoumoglou, Dimitrios Karageorgiou, Symeon Papadopoulos et al.

Recent advancements in generative AI have made text-guided image inpainting - adding, removing, or altering image regions using textual prompts - widely accessible. However, generating semantically correct photorealistic imagery, typically requires carefully-crafted prompts and iterative refinement by evaluating the realism of the generated content - tasks commonly performed by humans. To automate the generative process, we propose Semantically Aligned and Uncertainty Guided AI Image Inpainting (SAGI), a model-agnostic pipeline, to sample prompts from a distribution that closely aligns with human perception and to evaluate the generated content and discard instances that deviate from such a distribution, which we approximate using pretrained large language models and vision-language models. By applying this pipeline on multiple state-of-the-art inpainting models, we create the SAGI Dataset (SAGI-D), currently the largest and most diverse dataset of AI-generated inpaintings, comprising over 95k inpainted images and a human-evaluated subset. Our experiments show that semantic alignment significantly improves image quality and aesthetics, while uncertainty guidance effectively identifies realistic manipulations - human ability to distinguish inpainted images from real ones drops from 74% to 35% in terms of accuracy, after applying our pipeline. Moreover, using SAGI-D for training several image forensic approaches increases in-domain detection performance on average by 37.4% and out-of-domain generalization by 26.1% in terms of IoU, also demonstrating its utility in countering malicious exploitation of generative AI. Code and dataset are available at https://mever-team.github.io/SAGI/

Christodoulos Kechris, Alexandros Delitzas, Vasileios Matsoukas et al.

Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting. To this end, we propose an end-to-end deep learning approach to the problem, utilizing a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals, outperforming widely-used wavelet denoising techniques.