[AIxIA] [CFP] JDIQ: Special Issue on "Securing the Information Ecosystem: Emerging Advances in the Detection and Mitigation of AI-Generated Content"

[Chiara Pero]

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*** Call for Papers - Journal of Data and Information Quality (JDIQ), ACM Journal ***

Special Issue on Securing the Information Ecosystem: Emerging Advances in the Detection and Mitigation of AI-Generated Content

PAPER SUBMISSION DEADLINE:  31/10/2026

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OVERVIEW

The increasing availability of powerful generative and multimodal AI systems has introduced new and complex challenges for data and information quality. Foundation models capable of producing realistic text, images, audio, and video now operate at scale, reshaping how information is created, disseminated, and consumed. While these technologies offer clear benefits for automation and knowledge production, they also systematically blur the boundaries between authentic and synthetic information, creating new risks for reliability, provenance, consistency, and trust. This Special Issue focuses on the quality of information in environments saturated with AI-generated and AI-manipulated content. Rather than addressing synthetic data from a data-augmentation perspective, the issue investigates how generative technologies affect fidelity, consistency, verifiability, traceability, and usability of information across complex data pipelines and socio-technical systems. The coverage includes methodological, theoretical, and applied research that develops quality-aware detection, verification, and assessment frameworks for machine-generated and machine-altered content. Contributions are encouraged that examine robustness against adversarial manipulation, explainability of quality judgments, multimodal integrity checking, and scalable evaluation practices. The overarching objective is to provide a coherent scientific foundation for preserving and restoring trustworthy information flows in data-intensive applications shaped by modern generative AI.

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SPECIAL ISSUE TOPICS

Topics include but are not limited to:

• Detection and classification of AI-generated or AI-manipulated media (text, image, video, audio) with focus on information quality and authenticity.
• Multimodal information quality assessment: frameworks for verifying consistency and coherence across different data modalities (e.g., text + image, audio + video), especially in the presence of synthetic content.
• Adversarial robustness and security-aware data/information quality: defense methods and evaluation frameworks for content authenticity under adversarial or malicious manipulation.
• Explainable and interpretable approaches for content verification and information quality judgments, including provenance tracking and traceability metadata.
• Decentralized, federated, or distributed paradigms for misinformation detection and content verification, with emphasis on trust, privacy, and data quality under distributed data sources.
• Secure provenance and content authentication mechanisms (e.g., blockchain or cryptographic techniques) for ensuring information lineage, ownership, and tamper resistance.
• Human–AI collaborative systems and hybrid verification frameworks: combining automated detection with human oversight to calibrate trust and quality judgments.
• Case studies and applied research on information quality in domains impacted by AI- generated content: journalism, social media, finance, cybersecurity, digital media, etc.
• Domain-specific implementations and empirical evaluations: studies measuring the impact of AI-generated content on information quality, and assessing mitigation strategies in real- world environments.

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IMPORTANT DATES

• Submission deadline: 31/10/2026

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GUEST EDITORS

• Carmen Bisogni, University of Salerno, Italy, cbis...@unisa.it
• Chiara Pero, Link Campus University, Italy, c.p...@unilink.it

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SUBMISSION INFORMATION

JDIQ welcomes manuscripts that extend prior published work, provided they contain at least 30% new material, and that the significant new contributions are clearly identified in the introduction. Submission guidelines with Latex (preferred) or Word templates are available at https://dl.acm.org/journal/jdiq/author-guidelines#subm

For questions and further information, please contact Carmen Bisogni cbis...@unisa.it