arXiv:2604.17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods. In this paper, we use an ensemble of fine-tuned vision transformers like DINOv2, AIMv2 and OpenCLIP's ViT-L/14 to create generalizable method to detect deepfakes. We use the DF-Wild dataset released as part of the IEEE SP Cup 2025, because it uses a challenging and diverse set of manipulations and generation techniques. We started our experiments wi
The rapid advancement of generative AI models necessitates equally rapid development in deepfake detection, making this a critical area of research right now.
Improving deepfake detection is crucial for maintaining informational integrity and trust in digital media, impacting sectors from national security to social media platforms.
Deepfake detection methods are evolving from less generalizable techniques to more robust, AI-driven ensemble approaches, enhancing their ability to keep pace with generative AI.
- · Cybersecurity firms
- · Social media platforms
- · Fact-checking organizations
- · Generative AI researchers
- · Malicious deepfake creators
- · Outdated deepfake detection technologies
More effective deepfake detection tools become available to combat misinformation.
Public trust in online media may partially recover as egregious deepfakes become easier to identify.
The cat-and-mouse game between deepfake generation and detection accelerates, potentially driving further AI innovation in both areas.
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Read at arXiv cs.AI