A Vision Transformer-based binary classification model specifically designed for detecting deepfake images, with an accuracy rate of 99.2%

Computer VisionTransformers

This is a transformers model hosted on the Hugging Face Hub. Specific functionalities and uses require further information.

Natural Language ProcessingTransformers

A deepfake audio detection model fine-tuned based on wav2vec2-base, achieving 99.66% accuracy

Audio ProcessingTransformers

A binary classification model using fine-tuned Vision Transformer (ViT) to detect deepfake images

Computer VisionSafetensors

A fine-tuned version based on MCG-NJU/videomae-large model on the deepfake detection challenge dataset, used for video deepfake detection.

Computer VisionTransformers

This is a multi-modal forgery representation learning model focused on image-text to text processing, used to detect video content generated by diffusion models, and identify deepfake content through multi-modal feature learning.

MultimodalTransformers

FLODA is an advanced deepfake detection model that integrates image caption generation and authenticity assessment functions, achieving high-precision detection through visual question answering tasks.

MultimodalSafetensorsEnglish

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, used to identify synthetic or tampered speech content

Audio ProcessingTransformers

A Vision Transformer model for detecting deepfake images, achieving high-precision forgery detection through contrastive learning and global-local similarity analysis.

Computer VisionTransformers

A Deepfake audio detection model fine-tuned on audio folder datasets, achieving 99.73% accuracy

Audio ProcessingTransformers

A Deepfake audio detection model fine-tuned on audio folder datasets, capable of efficiently identifying synthetic speech with an evaluation accuracy of 99.64%

Audio ProcessingTransformers

A speech processing model further fine-tuned based on wav2vec2-base-finetuned, achieving 98.82% accuracy on the evaluation set

Audio ProcessingTransformers

A fine-tuned speech processing model based on wav2vec2-base-finetuned, achieving 98.82% accuracy on the evaluation set

Audio ProcessingTransformers

An audio classification model based on the wav2vec2 architecture, fine-tuned for deepfake audio detection tasks, excelling in gender recognition and fake audio detection.

Audio ProcessingTransformers

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, achieving 95.45% accuracy on the evaluation set

Audio ProcessingTransformers

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, trained on a multi-ethnic English dataset, capable of effectively identifying forged audio, with an evaluation accuracy of 95.45%.

Audio ProcessingTransformers

A deepfake audio detection model fine-tuned based on facebook/wav2vec2-base, achieving 95.45% accuracy on the evaluation set

Audio ProcessingTransformers

A deepfake image detection model fine-tuned based on the SigLIP2 vision-language encoder for binary classification of real vs. synthetic images

Computer VisionTransformersEnglish

A deepfake audio detection model fine-tuned based on Microsoft's WavLM-base, achieving excellent performance on the ASVspoof 2019 dataset with an accuracy of 99.79%

Audio ProcessingTransformers

An audio classification model based on the HuBERT architecture, specifically designed for detecting deepfake audio and audio spoofing

Audio ProcessingTransformers