Multimedia Security in the Era of Artificial Intelligence Generated Content

Dear Colleagues,

The rapid evolution of artificial-intelligence-generated content (AIGC) has ushered in transformative opportunities across various domains, including media production, virtual communication, and creative industries. However, this technological leap also introduces unprecedented challenges in multimedia security, particularly concerning authenticity, trustworthiness, and accountability. The proliferation of deepfakes, synthetic media, and generative-AI-based manipulation tools has raised critical concerns in digital forensics, content verification, and societal trust.

This Special Issue aims to gather cutting-edge research addressing the security and forensic challenges posed by AIGC. It seeks to explore novel methodologies, system-level solutions, and theoretical foundations that ensure integrity, traceability, and explainability of multimedia content in this new era. 

While AIGC technologies (e.g., GANs, diffusion models, large language models, and multimodal transformers) are becoming increasingly sophisticated, existing detection and attribution methods often fall short in terms of robustness, scalability, and generalizability. There is a critical need for secure and explainable mechanisms that can detect, localize, attribute, and mitigate malicious uses of generative content. Additionally, proactive strategies such as watermarking, provenance tracking, and secure content generation are gaining attention.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Overview:

• AIGC Forensics and Detection
  - Deepfake detection and localization;
  - Detection of synthetic images, audio, video, and text;
  - Cross-modal and multimodal detection techniques;
  - Generalization and robustness of forensic detectors.
• Security in AIGC Generation
  - Secure and trustworthy generative models;
  - Adversarial robustness of AIGC systems;
  - Privacy-preserving AIGC;
  - Defense against prompt injection and misuse in LLMs.
• Attribution, Provenance, and Watermarking
  - Source attribution of generative content;
  - Passive and active watermarking for AIGC;
  - Provenance tracking and tamper evidence;
  - Digital signatures and content authentication.
• Explainability and Interpretability
  - Explainable deepfake and AIGC detection;
  - Human–AI collaboration in content verification;
  - Visual and semantic rationale generation for forensic decisions.
• Datasets, Benchmarks, and Evaluation
  - Creation of large-scale and realistic AIGC forensic datasets;
  - Benchmarking protocols and performance metrics;
  - Real-world deployment and evaluation case studies.
• Ethical, Legal, and Societal Implications
  - Policy frameworks and standards for AIGC security;
  - Legal implications of synthetic media;
  - Detection and mitigation of AIGC-based misinformation.

We look forward to receiving your contributions.

Dr. Chenqi Kong
Dr. Anwei Luo
Guest Editors

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• AIGC
• security
• forensics
• deepfake
• multimodal
• detection
• localization
• traceability
• explainability