Semantic Inconsistencies Research Articles

Detection of semantic inconsistencies of motor actions: From language to praxis

Semantics of actions includes three types of knowledge: a) of the function of objects and tools, b) of the actions independently of the tools, and c) of the organization of simple actions in sequences. These types of knowledge might be structured as thematic roles into the semantics of actions. To test these hypothesis, 125 illustrations, divided into five conditions: i) Congruent (C), ii) Agent Inconsistent (AI), iii) Instrument Inconsistent (II), iv) Patient Inconsistent (PI), and v) Location Inconsistent (LI), were presented to 23 volunteers (50 % women), aged 20–25, who were asked to respond whether the image was congruent or incongruent. Electrical brain activity was recorded through 20 channels to obtain the event-related potentials (ERP) associated. Lower reaction times for II and PI than C, and a greater number of incorrect trials for C were found. A N300/N400 effect appeared for AI and LI conditions with respect to C. Finally, II and LI conditions present a deflected P600 in reference to C. These findings suggest semantic of actions is sensitive to thematic role manipulations and constitute evidence in favor of a semantic processing shared between visually observed praxis and words.

A SYSTEMATIC REVIEW OF BIG DATA INTEGRATION CHALLENGES AND SOLUTIONS FOR HETEROGENEOUS DATA SOURCES

This systematic review explores the current challenges and emerging solutions in big data integration, focusing on key issues such as semantic heterogeneity, data quality, scalability, and security. Using the PRISMA guidelines, 150 peer-reviewed articles were analyzed to identify both established and innovative approaches to integrating data from heterogeneous sources. The findings reveal that ontology-based frameworks are widely used to address semantic inconsistencies but face limitations in scalability when handling large, dynamic datasets. Machine learning has emerged as a powerful tool for automating data quality and schema matching processes, although its effectiveness is highly dependent on the availability of high-quality training data. Distributed computing frameworks like Hadoop and Spark have become the industry standard for scalable data integration, yet their implementation requires significant infrastructure and technical expertise. Cloud-based platforms offer flexible, scalable solutions, but concerns about data privacy and security persist. Blockchain technology, while promising for secure and decentralized data integration, is still in its infancy and struggles with scalability. The review highlights significant progress in the field but underscores the need for further research to address unresolved challenges in real-time integration, cross-domain data harmonization, and the management of unstructured data.

Motion-guided semantic alignment for line art animation colorization

Line art animation colorization aims to colorize a sequence of line art frames with a reference while ensuring semantic alignment across frames. Recent methods have utilized similarity metrics to maintain inter-frame semantic alignment. However, these approaches typically struggle with precise pixel-to-pixel motion alignment, resulting in issues like semantic inconsistencies and color blurring. To address these problems, we introduce an innovative Motion-Guided Colorization Network(MGC-Net), which incorporates line art motions into account for achieving coarse to fine semantic alignment. At the coarse level, our approach includes a Hierarchical Cost-feature Distillation (HCD) module. This module is designed to establish accurate pixel-to-pixel motion alignment between frames. It employs a motion distillation loss, which effectively transfers accurate color motion representation to line arts at varying hierarchical levels. Specifically, we utilize the cost features in inter-frames as a representation of motion, processed through our self-supervision operator. At the finer level, we propose the Correlation Refinement (CR) module. This module enhances semantic alignment by integrating semantic contextual dependencies between the color reference and the motion-based reference obtained from our motion-based confidence estimation block. CR module refines the final colorization process, improving clear color detail and semantic consistency across frames. Quantitative and qualitative experiments on animation datasets show that MGC-Net outperforms the state-of-the-art methods.

Cross-attention guided loss-based deep dual-branch fusion network for liver tumor classification

Recently, convolutional neural networks (CNNs) and multiple instance learning (MIL) methods have been successfully applied to MRI images. However, CNNs directly utilize the whole image as the model input and the downsampling strategy (like max or mean pooling) to reduce the size of the feature map, thereby possibly neglecting some local details. And MIL methods learn instance-level or local features without considering spatial information. To overcome these issues, in this paper, we propose a novel cross-attention guided loss-based dual-branch framework (LCA-DB) to leverage spatial and local image information simultaneously, which is composed of an image-based attention network (IA-Net), a patch-based attention network (PA-Net) and a cross-attention module (CA). Specifically, IA-Net directly learns image features with loss-based attention to mine significant regions, meanwhile, PA-Net captures patch-specific representations to extract crucial patches related to the tumor. Additionally, the cross-attention module is designed to integrate patch-level features by using attention weights generated from each other, thereby assisting them in mining supplement region information and enhancing the interactive collaboration of the two branches. Moreover, we employ an attention similarity loss to further reduce the semantic inconsistency of attention weights obtained from the two branches. Finally, extensive experiments on three liver tumor classification tasks demonstrate the effectiveness of the proposed framework, e.g., on the LLD-MMRI–7, our method achieves 69.2%, 65.9% and 88.5% on the seven-class liver tumor classification tasks in terms of accuracy, F1 score and AUC, with the superior classification and interpretation performance over recent state-of-the-art methods. The source code of LCA-DB is available at https://github.com/Wangrui-berry/Cross-attention.

A Novel Skip-Connection Strategy by Fusing Spatial and Channel Wise Features for Multi-Region Medical Image Segmentation.

Recent methods often introduce attention mechanisms into the skip connections of U-shaped networks to capture features. However, these methods usually overlook spatial information extraction in skip connections and exhibit inefficiency in capturing spatial and channel information. This issue prompts us to reevaluate the design of the skip-connection mechanism and propose a new deep-learning network called the Fusing Spatial and Channel Attention Network, abbreviated as FSCA-Net. FSCA-Net is a novel U-shaped network architecture that utilizes the Parallel Attention Transformer (PAT) to enhance the extraction of spatial and channel features in the skip-connection mechanism, further compensating for downsampling losses. We design the Cross-Attention Bridge Layer (CAB) to mitigate excessive feature and resolution loss when downsampling to the lowest level, ensuring meaningful information fusion during upsampling at the lowest level. Finally, we construct the Dual-Path Channel Attention (DPCA) module to guide channel and spatial information filtering for Transformer features, eliminating ambiguities with decoder features and better concatenating features with semantic inconsistencies between the Transformer and the U-Net decoder. FSCA-Net is designed explicitly for fine-grained segmentation tasks of multiple organs and regions. Our approach achieves over 48% reduction in FLOPs and over 32% reduction in parameters compared to the state-of-the-art method. Moreover, FSCA-Net outperforms existing segmentation methods on seven public datasets, demonstrating exceptional performance.

Specificity of Translating Icononyms into Diverse Structural Languages (English, French, and Tatar)

This study examines proper names functioning as titles of iconographic works (such as ‘Pokrov Bogomateri’ (the Protection of the Mother of God), ‘Voskreseniye Khristovo’ (the Resurrection of Christ), ‘Soshestviye vo ad’ (the Descent into Hell) etc.) in the context of exploring their translation into English, French, and Tatar. A comparative analysis of artionyms (icononyms) is conducted. The sources for this research include catalogs of ancient Russian painting, particularly those from the collection of the State Museum of Fine Arts of the Republic of Tatarstan (Kazan). The paper discusses the challenges involved in translating terms such as ‘umileniye’ (compassion), ‘pokrov’ (protection), and ‘успение’ (dormition) etc. On one hand, doctrinal discrepancies are noted in the target languages, which are attributed to extralinguistic factors (differences in approaches to key Christian doctrines among Orthodoxy, Protestantism, and Catholicism, as well as the absence of cultural elements from the source language in the target language). On the other hand, semantic inconsistencies are identified, which can be explained by attempts at generalization or, conversely, specification of the content of terms aimed at facilitating recipient comprehension. One of the most frequent translational transformations observed is calquing, with identified differences in word formation patterns explained by the distinct typological models of the languages being compared (synthetic, analytic, agglutinative).

SemID: Blind Image Inpainting with Semantic Inconsistency Detection

SemID: Blind Image Inpainting with Semantic Inconsistency Detection

Rethinking Self-Supervised Semantic Segmentation: Achieving End-to-End Segmentation.

The challenge of semantic segmentation with scarce pixel-level annotations has induced many self-supervised works, however most of which essentially train an image encoder or a segmentation head that produces finer dense representations, and when performing segmentation inference they need to resort to supervised linear classifiers or traditional clustering. Segmentation by dataset-level clustering not only deviates the real-time and end-to-end inference practice, but also escalates the problem from segmenting per image to clustering all pixels at once, which results in downgraded performance. To remedy this issue, we propose a novel self-supervised semantic segmentation training and inferring paradigm where inferring is performed in an end-to-end manner. Specifically, based on our observations in probing dense representation by image-level self-supervised ViT, i.e. semantic inconsistency between patches and poor semantic quality in non-salient regions, we propose prototype-image alignment and global-local alignment with attention map constraint to train a tailored Transformer Decoder with learnable prototypes and utilize adaptive prototypes for segmentation inference per image. Extensive experiments under fully unsupervised semantic segmentation settings demonstrate the superior performance and the generalizability of our proposed method. The code is available at: https://github.com/yliu1229/AlignSeg.

Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.

Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies. https://github.com/jiyuc/de-inconsistency.

Electrophysiological markers of orthographic pattern learning in school-aged children with reading challenges: An ERP investigation

BackgroundPrevious studies suggested that children with reading difficulty have impaired statistical learning ability in extracting distributional orthographic regularities. However, the neural mechanisms underlying have not been fully investigated. AimsThe current study aimed to identify the electrophysiological markers and to examine the neural underpinnings of statistical learning of orthographic regularities in children with reading difficulties. Methods and proceduresUsing the event-related potentials (ERPs) and the orthographic learning task, 157 children were exposed to a sequence of artificial pseudocharacters with varying levels of positional and semantic consistency (low at 60 %, moderate at 80 %, and high at 100 %). Outcomes and resultsPoor readers elicited an increased N170 response in the low consistency and a lack of left-lateralized P300 effect when learning positional regularities of radicals. Similarly, larger N170 effects were observed in poor readers, while similar N400 effects were found in both poor and average readers when learning semantic regularities of radicals. Conclusions and implicationsOur findings indicate that poor readers may have trouble using statistical information for early-stage orthographic pattern extraction, yet they can identify semantic inconsistencies after sufficient exposure. These results deepen our understanding of the neural mechanisms involved in statistical learning for poor readers and aid in improving criteria for differentiating between typically developing children and those with reading challenges.

Cross-modal semantic aligning and neighbor-aware completing for robust text–image person retrieval

Most existing text–image person re-identification (TIReID) methods are performed in an ideal environment where both image and text instances are fully intact and identity annotated. However, in real-world open environments, these ideal assumptions often cannot be satisfied. In this study, we are the first to explore how to enhance the robustness of the TIReID model to better adapt to open environments, focusing on two aspects: 1) unlabeled multi-modal data, and 2) incomplete multi-modal data. Here, unlabeled multi-modal data faces two key issues: intra-class variations and cross-modal aligning problems, while incomplete data leads to data semantic inconsistency problems and severe performance degradation. In order to tackle the above issues, we propose a novel Cross-modal Semantic Aligning and Neighbor-aware Completing (CANC) method for robust text–image person re-identification (RTIReID). Specifically, to tackle the intra-class variation problem, we propose intra-view prototype contrastive matching for image and text modalities, which helps the model learn intra-modal discriminative representations while increasing inter-class distance and suppressing intra-class variation. Furthermore, to resolve the cross-modal alignment problem, we propose the cross-view instance projection matching to establish the relationship between different modal representations. Additionally, regarding how to effectively handle incomplete samples, we introduce nearest neighbor consistent completion, which can restore high-quality completion features. We adopt CLIP-ViT and CLIP-Xformer as image and text encoders respectively, and Rank-k metrics (k=1,5,10) as primary evaluation metrics. Extensive experiments on several public datasets demonstrate that our method can effectively address TIReID in open environments, consistently surpassing several SOTA TIReID methods.

Augmentation blending with clustering-aware outlier factor: An outlier-driven perspective for enhanced contrastive learning

Data augmentation is an effective strategy to improve model performance and generalization capabilities by expanding both the size and diversity of the original dataset via subtle modifications to the original data. In mainstream contrastive learning frameworks, data augmentation is leveraged by default to compose positive and negative pairs. However, selecting optimal augmentations that consistently preserve the semantics of the original samples poses challenges owing to variations in the quality of different augmentation transformations. To address this challenge, we provide a novel perspective and formulate the data augmentation optimization problem as an outlier-driven augmentation blending problem. Specifically, we design an innovative metric named Clustering-Aware Outlier Factor (CAOF), to quantify the semantic inconsistency between the augmented samples and original samples. CAOF is capable of identifying semantically inconsistent outliers in augmented samples that are generated by various transformations, thereby offering better context-aware density assessments by considering the intra-cluster neighbors for individual samples. Each augmentation transformation is evaluated using the proposed CAOF, which reflects the likelihood of producing semantically inconsistent augmented samples. The likelihood determines the importance of the transformation in generating a refined blended augmentation, which is subsequently fed into the contrastive learning framework. We conduct empirical evaluations on six time series datasets. We compare our method with three state-of-the-art augmentation selection methods on five contrastive learning models. The results demonstrate the superiority of our proposed approach in discovering outliers and refining augmentations in contrastive learning for time series classification tasks.

Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.

Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.

Weight Saliency search with Semantic Constraint for Neural Machine Translation attacks

Text adversarial attack is an effective way to improve the robustness of Neural Machine Translation (NMT) models. Existing NMT attack tasks are often completed by replacing words. However, most of previous works pursue a high attack success rate but produce semantic inconsistency sentences, leading to wrong translations even for humans. In this paper, we propose a Weight Saliency search with Semantic Constraint (WSSC) algorithm to make semantic consistency word modifications to the input sentence for black-box NMT attacks. Specifically, our WSSC has two major merits. First, it optimizes the word substitution with a word saliency method, which is helpful to reduce word replacement rate. Second, it constrains the objective function with a semantic similarity loss, ensuring every modification does not lead to significant semantic changes. We evaluate the effectiveness of the proposed WSSC by attacking three popular NMT models, i.e., T5, Marian, and BART, on three widely used datasets, i.e., WMT14, WMT16, and TED. Experimental results validate that our WSSC improves Attack Success Rate (ASR) by 4.02% and Semantic Similarity score (USE) by 1.28% on average. Besides, our WSSC also shows good properties in keeping grammar correctness and transfer attack.

Towards accurate binocular vision of satellites: A cascaded multi-scale pyramid network for stereo matching on satellite imagery

Stereo matching of high-resolution satellite stereo images is a fundamental and challenging task in photogrammetry, geoscience, and remote sensing. Some deep learning-based methods have shown great potential in solving this task recently. However, these methods have not attracted much attention as their accuracy is still unsatisfactory, especially in regions with intractable factors (e.g., occlusions, repetitive patterns, low texture, non-texture, and disparity discontinuities). To tackle these challenging factors and improve disparity accuracy, we propose an end-to-end stereo matching network named Cascaded Multi-Scale Pyramid Network (CMSP-Net). Firstly, to fully utilize the multi-level spatial context information and mitigate the ambiguous matching caused by repetitive patterns, low texture, non-texture, and occlusions, a cost volume pyramid is constructed with multi-scale image features. Then, each cost volume in the pyramid is aggregated with an hourglass structure to exploit the multi-scale context in a single cost volume. Secondly, considering that high-resolution cost volumes contain more detailed information but have small receptive fields, while low-resolution ones have large receptive fields but lack details, we design an Attention-Guided Cost Fusion (AGCF) strategy to fuse pyramid cost volumes, which effectively integrates the advantages of multi-scale cost volumes while mitigating the backfiring caused by semantic inconsistency across different scales. Thirdly, a disparity refinement module is designed to improve overall disparity accuracy further, which incorporates the reconstruction error and edge clues of input satellite stereo images. Extensive experimental results on multiple satellite datasets demonstrate that the proposed method can effectively reduce matching ambiguities and recover sharp disparity discontinuities, achieving clear superiority over existing state-of-the-art methods.

Retinal disease diagnosis with unsupervised Grad-CAM guided contrastive learning

To alleviate the reliance on expensive annotations, contrastive learning techniques have been applied to diagnose diseases from various types of medical images. However, popular contrastive learning methods, which generate positive pairs by random cropping, face challenges in diagnosing retinal diseases, due to the retinal disease’s characteristic that retinal lesions are tiny and randomly distributed in abnormal fundus images. These lesions may be missed after random cropping, resulting in semantically inconsistent positive pairs that hinder the effectiveness of contrastive learning for retinal disease diagnosis. To address this issue, we propose a novel unsupervised gradient-weighted class activation mapping (Grad-CAM) strategy to roughly locate lesions, thereby suppressing or even eliminating semantically inconsistent positive pairs. Specifically, we develop a gradient-weighted Class Activation Map guided Contrastive Learning (CAMCL) method with two branches for the Grad-CAM based instance discrimination task and the k-nearest neighbors (KNN)-based cluster-wise discrimination task, respectively. By minimizing the KNN loss, the cluster-wise discrimination branch learns high-level representations containing class semantic information. This is then followed by gradient back-propagation to generate Grad-CAM heatmaps from unlabeled data. The generated heatmaps can highlight class-discriminative regions in abnormal fundus images (e.g., retinal lesions) to identify semantically consistent positive pairs while suppressing inconsistent ones. The semantically consistent positive pairs are then input to the instance discrimination task for contrastive learning. In this manner, the semantic inconsistency problem is relieved, and the improved contrastive learning pipeline can be effectively used for retinal disease diagnosis. Experimental results on five retinal disease classification datasets show that our model surpasses other contrastive learning methods, indicating a promising approach for clinical application.

Understanding and Finding Java Decompiler Bugs

Java decompilers are programs that perform the reverse process of Java compilers, i.e., they translate Java bytecode to Java source code. They are essential for reverse engineering purposes and have become more sophisticated and reliable over the years. However, it remains challenging for modern Java decompilers to reliably perform correct decompilation on real-world programs. To shed light on the key challenges of Java decompilation, this paper provides the first systematic study on the characteristics and causes of bugs in mature, widely-used Java decompilers. We conduct the study by investigating 333 unique bugs from three popular Java decompilers. Our key findings and observations include: (1) Although most of the reported bugs were found when decompiling large, real-world code, 40.2% of them have small test cases for bug reproduction; (2) Over 80% of the bugs manifest as exceptions, syntactic errors, or semantic errors, and bugs with source code artifacts are very likely semantic errors; (3) 57.7%, 39.0%, and 41.1% of the bugs respectively are attributed to three stages of decompilers—loading structure entities from bytecode, optimizing these entities, and generating source code from these entities; (4) Bugs in decompilers’ type inference are the most complex to fix; and (5) Region restoration for structures like loop, sugaring for special structures like switch, and type inference of variables of generic types or indistinguishable types are the three most significant challenges in Java decompilation, which to some extent explains our findings in (3) and (4). Based on these findings, we present JD-Tester, a differential testing framework for Java decompilers, and our experience of using it in testing the three popular Java decompilers. JD-Testerutilizes different Java program generators to construct executable Java tests and finds exceptions, syntactic, and semantic inconsistencies (i.e. bugs) between a generated test and its compiled-decompiled version (through compilation and execution). In total, we have found 62 bugs in the three decompilers, demonstrating both the effectiveness of JD-Tester, and the importance of testing and validating Java decompilers.

Exploring the Semantic-Inconsistency Effect in Scenes Using a Continuous Measure of Linguistic-Semantic Similarity

Viewers use contextual information to visually explore complex scenes. Object recognition is facilitated by exploiting object–scene relations (which objects are expected in a given scene) and object–object relations (which objects are expected because of the occurrence of other objects). Semantically inconsistent objects deviate from these expectations, so they tend to capture viewers’ attention (the semantic-inconsistency effect). Some objects fit the identity of a scene more or less than others, yet semantic inconsistencies have hitherto been operationalized as binary (consistent vs. inconsistent). In an eye-tracking experiment (N = 21 adults), we study the semantic-inconsistency effect in a continuous manner by using the linguistic-semantic similarity of an object to the scene category and to other objects in the scene. We found that both highly consistent and highly inconsistent objects are viewed more than other objects (U-shaped relationship), revealing that the (in)consistency effect is more than a simple binary classification.

An improved deep hashing model for image retrieval with binary code similarities

The exponential growth of data raises an unprecedented challenge in data analysis: how to retrieve interesting information from such large-scale data. Hash learning is a promising solution to address this challenge, because it may bring many potential advantages, such as extremely high efficiency and low storage cost, after projecting high-dimensional data to compact binary codes. However, traditional hash learning algorithms often suffer from the problem of semantic inconsistency, where images with similar semantic features may have different binary codes. In this paper, we propose a novel end-to-end deep hashing method based on the similarities of binary codes, dubbed CSDH (Code Similarity-based Deep Hashing), for image retrieval. Specifically, it extracts deep features from images to capture semantic information using a pre-trained deep convolutional neural network. Additionally, a hidden and fully connected layer is attached at the end of the deep network to derive hash bits by virtue of an activation function. To preserve the semantic consistency of images, a loss function has been introduced. It takes the label similarities, as well as the Hamming embedding distances, into consideration. By doing so, CSDH can learn more compact and powerful hash codes, which not only can preserve semantic similarity but also have small Hamming distances between similar images. To verify the effectiveness of CSDH, we evaluate CSDH on two public benchmark image collections, i.e., CIFAR-10 and NUS-WIDE, with five classic shallow hashing models and six popular deep hashing ones. The experimental results show that CSDH can achieve competitive performance to the popular deep hashing algorithms.

Exif2Vec: A Framework to Ascertain Untrustworthy Crowdsourced Images Using Metadata

In the context of social media, the integrity of images is often dubious. To tackle this challenge, we introduce Exif2Vec , a novel framework specifically designed to discover modifications in social media images. The proposed framework leverages an image’s metadata to discover changes in an image. We use a service-oriented approach that considers discovery of changes in images as a service . A novel word-embedding-based approach is proposed to discover semantic inconsistencies in an image metadata that are reflective of the changes in an image. These inconsistencies are used to measure the severity of changes. The novelty of the approach resides in that it does not require the use of images to determine the underlying changes. We use a pretrained Word2Vec model to conduct experiments. The model is validated on two different fact-checked image datasets, i.e., images related to general context and a context-specific image dataset. Notably, our findings showcase the remarkable efficacy of our approach, yielding results of up to 80% accuracy. This underscores the potential of our framework.