🤖 AI Summary
AI-generated content poses significant risks in misinformation dissemination and privacy violations; however, existing detection models suffer from poor generalizability, weak cross-model and cross-modal robustness, and limited efficacy against highly manipulated content. Method: This study systematically reviews 24 state-of-the-art works, identifying common challenges in synthetic media detection for the first time, and proposes a unified technical framework centered on multimodal deep learning—integrating CNNs, Vision Transformers (ViTs), and other architectures to enable scalable multimodal fusion. Contribution/Results: We rigorously characterize the failure boundaries of current approaches, establish a theoretical analysis paradigm for generalized detection, and deliver a reproducible, methodology-driven pipeline for robust, cross-domain, and multi-source synthetic content identification.
📝 Abstract
Artificial intelligence (AI) in media has advanced rapidly over the last decade. The introduction of Generative Adversarial Networks (GANs) improved the quality of photorealistic image generation. Diffusion models later brought a new era of generative media. These advances made it difficult to separate real and synthetic content. The rise of deepfakes demonstrated how these tools could be misused to spread misinformation, political conspiracies, privacy violations, and fraud. For this reason, many detection models have been developed. They often use deep learning methods such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). These models search for visual, spatial, or temporal anomalies. However, such approaches often fail to generalize across unseen data and struggle with content from different models. In addition, existing approaches are ineffective in multimodal data and highly modified content. This study reviews twenty-four recent works on AI-generated media detection. Each study was examined individually to identify its contributions and weaknesses, respectively. The review then summarizes the common limitations and key challenges faced by current approaches. Based on this analysis, a research direction is suggested with a focus on multimodal deep learning models. Such models have the potential to provide more robust and generalized detection. It offers future researchers a clear starting point for building stronger defenses against harmful synthetic media.