Toward Generalized Detection of Synthetic Media: Limitations, Challenges, and the Path to Multimodal Solutions

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Authors: Redwan Hussain, Mizanur Rahman, Prithwiraj Bhattacharjee

Published: 2025-11-14 09:44:44+00:00

AI Summary

This study conducts a review of twenty-four recent works on AI-generated media detection, analyzing their contributions, weaknesses, and common limitations, such as poor generalization across unseen data. The paper concludes that traditional unimodal approaches are insufficient against rapidly advancing synthetic media and suggests a vital research direction toward generalized, robust detection systems based on multimodal deep learning models.

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.

Key findings

Existing detection systems frequently fail to generalize across unseen data or different generation techniques, and current methods are often ineffective in handling subtle or highly modified multimodal content. The lack of diversified, large-scale datasets poses a significant barrier to developing generalized detectors. A future direction must involve multimodal deep learning architectures that fuse audio and visual cues for more robust performance.

Approach

The authors conducted a literature review and comparative analysis of twenty-four state-of-the-art detection papers to identify key challenges hindering progress, including computational constraints and the inability of models to capture temporal anomalies effectively. Based on the aggregated weaknesses, the paper proposes developing multimodal deep learning models as the most promising path toward generalized synthetic media detection.

Datasets

UNKNOWN

Model(s)

UNKNOWN

Author countries

Bangladesh

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