The advancement of artificial intelligence (AI) technologies, including generative pre-trained transformers (GPTs) and generative models for text, image, audio, and video creation, has revolutionized content generation, creating unprecedented opportunities and critical challenges. This paper systematically examines the characteristics, methodologies, and challenges associated with detecting the synthetic content across multiple modalities, to safeguard digital authenticity and integrity. Key detection approaches reviewed include stylometric analysis, watermarking, pixel prediction techniques, dual-stream networks, machine learning models, blockchain, and hybrid approaches, highlighting their strengths and limitations, as well as their detection accuracy, independent accuracy of 80% for stylometric analysis and up to 92% using multiple modalities in hybrid approaches. The effectiveness of these techniques is explored in diverse contexts, from identifying deepfakes and synthetic media to detecting AI-generated scientific texts. Ethical concerns, such as privacy violations, algorithmic bias, false positives, and overreliance on automated systems, are also critically discussed. Furthermore, the paper addresses legal and regulatory frameworks, including intellectual property challenges and emerging legislation, emphasizing the need for robust governance to mitigate misuse. Real-world examples of detection systems are analyzed to provide practical insights into implementation challenges. Future directions include developing generalizable and adaptive detection models, hybrid approaches, fostering collaboration between stakeholders, and integrating ethical safeguards. By presenting a comprehensive overview of AIGC detection, this paper aims to inform stakeholders, researchers, policymakers, and practitioners on addressing the dual-edged implications of AI-driven content creation.

The advent of sophisticated artificial intelligence technologies, including Generative Pre-trained Transformers for text and advanced generative models for image, audio, and video creation, have revolutionized content generation, presenting both innovative opportunities and significant challenges for information security. This paper introduces the characteristics, techniques, and challenges for detecting AI-generated content across text, image, audio, and video modalities, aiming to safeguard the integrity and authenticity of digital information.

Blockchain governance has become central to the performance and resilience of decentralized systems, yet current models face recurring issues of participation, coordination, and adaptability. This article offers a structured analysis of governance frameworks and highlights their limitations through recent high-impact case studies. It then examines how artificial intelligence (AI) is being integrated into governance processes, ranging from proposal summarization and anomaly detection to autonomous agent-based voting. In response to existing gaps, this paper proposes the Voting Via Parallel Predictive Agents (VOPPA) framework, a multi-agent architecture aimed at enabling predictive, diverse, and decentralized decision-making. Strengthening blockchain governance will require not just decentralization but also intelligent, adaptable, and accountable decision-making systems.

The rapid advancement of Generative Pre-trained Transformer (GPT) models has led to their widespread adoption across applied domains such as healthcare, finance, education, and enterprise software engineering. However, the large-scale data requirements and generative capabilities of these models introduce significant challenges related to data security, privacy preservation, and regulatory compliance. This paper presents a systematic literature review conducted in accordance with the PRISMA 2020 guidelines, analyzing 60 peer-reviewed empirical studies published between 2020 and 2025 in Q1 and Q2 journals indexed in the Web of Science Core Collection. The review examines the evolution of GPT architectures and evaluates state-of-the-art security and privacy techniques, including encryption, differential privacy, federated learning, data anonymization, model distillation, and secure deployment mechanisms. Key challenges identified include unintended memorization of sensitive data, adversarial prompt-based attacks, and performance degradation resulting from privacy-preserving constraints, with reported accuracy reductions ranging from 5% to 20% depending on the applied technique. Additionally, the analysis highlights increased computational overhead, in some cases exceeding 30–40% training or inference cost when advanced cryptographic methods are employed. Regulatory and ethical implications are assessed in relation to frameworks such as GDPR, CCPA, HIPAA, and the proposed EU Artificial Intelligence Act. The findings emphasize the need for privacy-by-design approaches and scalable governance strategies to support secure and trustworthy deployment of GPT models in applied real-world environments.