deepfake detector model v1 deepfake detector model v1 is a vision language encoder model fine tuned from google/siglip base patch16 512 for binary deepfake image classification. It is trained to detect whether an image is real or generated using synthetic media techniques. The model uses the SiglipForImageClassification architecture. [!warning] Experimental Label Space: 2 Classes The model classifies an image as one of the following: Install Dependencies Inference Code Intended Use deepfake detector model is designed for: Deepfake Detection – Accurately identify fake images generated by AI. Media Authentication – Verify the authenticity of digital visual content. Content Moderation – Assist in filtering synthetic media in online platforms. Forensic Analysis – Support digital forensics by detecting manipulated visual data. Security Applications – Integrate into surveillance systems for authenticity verification.
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