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Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study aims to examine the visual effects of cause-related marketing (CM) posts on Instagram, with a focus on image resolution and consumer engagement.

Three studies were conducted through an experimental design. Study 1 (N = 155) uncovered the mediation underlying the effects of image quality (low and high image resolution). Study 2 (N = 160) replicated the findings of the first study and extended the investigation by examining the mediator (fluency) and moderator (visual sensitivity). Study 3 (N = 291) further extended the effects of image resolution by demonstrating its interactive effects with the visual complexity of an Instagram post design in a 2 × 2 factorial experiment.

The serial mediation analysis demonstrated that high image resolution CM posts yielded more favorable evaluations in terms of brand credibility and information costs saved, subsequently leading to positive brand attitudes, purchase intentions and increased Instagram engagement. Processing fluency mediated image effects on brand credibility, while individual differences in visual sensitivity moderated the image effects. The image resolution effects were greater for visually complex CM posts compared to simple ones.

To one's best knowledge, little to no research has examined the image quality of Instagram posts in the context of CM and the extent to which such visual cues can affect consumers' brand evaluations and engagement on the platform.

Despite its practical significance, there exists a notable gap in understanding the specific role of CM posts on Instagram and the impact of visual elements on consumer behaviors. The current research findings aim to bridge the research gap.

The purpose of this study is to examine characteristics of retracted publications from Indian authors and inspect a relationship between journal impact factor (JIF) and the number of authors (NoA).

The authors examined the general characteristics of retracted publications and investigated the correlation between JIF and NoA from Indian authors from January 1, 2017, to December 31, 2022. Data were mined from retraction watch http://retractiondatabase.org/ (n = 1,459) and determined the year of publication, year of retraction, authors, journals, publishers and causes of the retractions. A journal citation report was extracted to gather the JIFs.

About one-third of retracted papers were published in 2020; 2022 has the highest retraction rate (723); studies with two authors represent about one-third (476) of the published articles; Journal of Ambient Intelligence and Humanized Computing (354) has the highest number of retractions; Springer published the most retracted papers (674); and the majority of the journal (1,133) is indexed in journal citation reports, with impact factor extending from 0.504 to 43.474. Retraction due to legal reasons/legal threats was the most predominant reason for retraction.

This study reflects growth in author collaborations with a surge in the JIF. This study recommends that quick retraction is essential to reduce the adverse effects of faulty research.

Facilitated by image retouch tools, social media influencers can digitally enhance their self-image in product recommendation posts. This paper proposes that image enhancement may serve as a cue for the audience to assess the authenticity of the influencer (“true to oneself”), which further affects the influencer's product recommendation effectiveness (i.e. attitudes toward the post and recommended product).

Experiment 1 examines the effect of image enhancement on consumers' perceived influencer authenticity and product recommendation effectiveness. Experiment 2 considers the moderating role of post type, examining the effects in informational versus storytelling posts.

Consumers perceived an influencer to be more authentic when the image is not enhanced; in turn, consumers reported more favorable attitudes toward the post and the recommended product upon reading the post. The effects are moderated by post type: the effect of image enhancement (through perceived influencer authenticity) exists in posts using an informational message format but is attenuated for those using a storytelling message format.

This research enriches the literature on authenticity cues by documenting a novel visual cue and contributes to influencer marketing by identifying a nuanced interactive effect between image enhancement and post type on recommendation effectiveness.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.

The present experimental study aims to investigate when a food safety incident occurs, how country image influences consumers' trust and purchase intention, as well as the relationship between trust and purchase intention.

Participants (N = 1,590) were randomly allocated into one of the eight conditions [(country competence: high vs low) × (country warmth: high vs low) × (clean green image: high vs low)], read the corresponding country image descriptions, and rated measures on trust in food safety and quality, and purchase intention of fruit imported from this exporting country before and after reading a fictional food safety incident scenario.

Results showed that the food safety incident led to a significant decrease in trust and purchase intention across all conditions. However, trust in food safety and quality, and purchase intention were still higher in high competence, warmth or clean green image conditions. The decreased magnitude of trust in food safety was larger when country competence and clean green image was high, and when country warmth was low. Food safety incident caused purchase intention to become more dependent on trust in food safety than food quality.

This study provides a novel insight into the impacts of food safety incidents on consumers' responses in different country image contexts including the human-related and environment-related dimensions.

Improving digital work experience is critical for the job performance of individuals and the competitiveness of organizations due to their increasing use. This paper investigates how organization support affects the digital work experience of individuals differently depending on their levels of information technology (IT) identity.

Drawing upon the IT identity literature and the conservation of resources (COR) theory, a conceptual model is developed, tested and validated using the data collected in Australia through an experimental design in which IT identity is manipulated.

This study reveals a nuanced impact of organization support on shaping digital work experience. Specifically, it finds that technical support is more effective in improving the digital work experience of individuals with a high level of IT identity, whereas well-being support is more effective in enhancing the digital work experience of individuals with a low level of IT identity.

This research contributes to the IT identity literature by introducing a novel experimental design to manipulate IT identity in the digital work context. It also contributes to the digital work literature by introducing a resource perspective for identifying well-being support, technical support and IT identity as the key resources in shaping digital work experience and calling for attention to IT identity as a boundary condition on the effectiveness of organization support. The findings can help organizations formulate better strategies and policies to improve digital work experience by providing tailored support to individuals with different levels of IT identity.

The brand logo, as a fundamental element of marketing communications, serves as a crucial visual representation of a brand. In the current era of mobile Internet, logo flatness has become a new trend in practice. However, there remains a scarcity of research that explores the effects of logo flatness on consumer perceptions and brand attitudes.

Across four studies, using both observational analyses of real brands and experimental manipulations of fictitious brands, the authors examined the impact of logo flatness on consumer perceptions and brand attitudes.

Results show that logo flatness promotes the perception of modernity due to the simplicity it presents. Consumers will evaluate the brand more positively when their perception of the logo association is congruent with the brand image. Notably, traditional brands using skeuomorphic logos and modern brands employing flat logos can effectively enhance consumers' brand attitudes.

The findings of this study have significant implications for businesses seeking to enhance consumers' brand attitude and foster brand renewal through the strategic selection and design of logos that align with their brand image.

This study provides a theoretical and empirical test of the influence of logo flatness on consumers' perception of brand image, thereby enriching the existing research on brand management.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

This study investigated the anthropomorphism of the pandemic virus and its downstream effects by examining how warnings trigger viewers' risk perceptions and motivate them to pursue protection.

Three experiments were conducted. The first was a two-part (virus: anthropomorphic vs non-anthropomorphic) between-subject design that measured the participants' risk perception and compliance intention. The second experiment used a three-part (cuteness: cute vs non-cute vs control) between-subjects design. The third experiment used a three-part (cuteness: cute vs non-cute vs control) by two-part (aggressive guidance: present vs absent) between-subject design.

Anthropomorphism of the virus increased risk perception, thus influencing protective behavior and the effectiveness of warning signs, but only when the message was not perceived as cute. Aggressive messages and cute images of baby schemata enhanced compliance intention to warning guidelines.

The results provide a theoretical basis for studying the effectiveness of anthropomorphized warning signs and suggest implications for the impact of anthropomorphism on risk communication and compliance.

This study highlights that cuteness, often accompanied by anthropomorphism, may evoke inferences that reduce the effect of risk communication to induce compliance intention. Furthermore, the authors discovered that a more persuasive message appeals to mitigate the maladaptive responses to cute warnings.