Home › Vision Models › deepfake-detector-model-v1

deepfake-detector-model-v1 - AI Vision Models Tool

Overview

deepfake-detector-model-v1 is a binary image-classification model fine-tuned from google/siglip2-base-patch16-512 to detect face and image manipulations commonly associated with deepfakes. The model uses the SiglipForImageClassification architecture and was adapted to output two labels — "fake" and "real" — intended for workflows such as media authentication, content moderation, forensic triage, and preliminary security screening. According to the model page on Hugging Face, it is distributed under the Apache-2.0 license, enabling broad reuse and commercial integration (see model page). The model is compact enough for practical inference: the published artifact lists approximately 92.9 million parameters and is packaged for the Transformers image-classification pipeline, making it straightforward to integrate into Python-based ML stacks. The Hugging Face repository reports community usage metrics (3,258 downloads and 27 likes at the time of writing), but the model card does not publish standardized accuracy or benchmark numbers, so users should validate performance on their target datasets and deployment conditions before relying on results for high-stakes decisions.

Model Statistics

Downloads: 3,258
Likes: 27
Pipeline: image-classification
Parameters: 92.9M

License: apache-2.0

Model Details

Architecture and base: fine-tuned from google/siglip2-base-patch16-512 using the SiglipForImageClassification interface (patch16, 512 input resolution). The implementation targets the Transformers image-classification pipeline and expects standard image inputs (PIL images, file paths, or numpy arrays). Output and labels: model outputs a probability score per class; class names are "fake" and "real". Consumers should interpret scores probabilistically (softmax over two logits). Size and runtime: the model contains ~92.9M parameters (as reported on the Hugging Face page) and is suitable for CPU inference for batch jobs and GPU inference for lower-latency scenarios. There is no official specification of FLOPs, quantized weights, or an optimized ONNX/TensorRT artifact on the model page. Training data and evaluation: the model card does not list a detailed public training dataset or standardized evaluation metrics. Because dataset provenance, class balance, and augmentation strategies are not documented on the model page, users must run domain-specific validation and, if needed, recalibration (threshold tuning or additional fine-tuning) before production use. License and reuse: distributed under Apache-2.0 (per model card), permitting commercial and derivative use with attribution per license terms. For legal or forensic use cases, corroborate outputs with complementary tools and human review.

Key Features

Binary 'fake' vs 'real' image classification for deepfake detection
Fine-tuned from google/siglip2-base-patch16-512 (SiglipForImageClassification)
Packaged for Transformers image-classification pipeline for easy integration
Apache-2.0 license allowing commercial and derivative use
Compact ~92.9M parameters for practical inference on CPU/GPU

Example Usage

Example (python):

from transformers import pipeline

# Create an image-classification pipeline that loads the fine-tuned detector
classifier = pipeline(
    task="image-classification",
    model="prithivMLmods/deepfake-detector-model-v1"
)

# Classify a local image (path) or PIL.Image object
result = classifier("/path/to/image.jpg")
print(result)

# Example output: [{'label': 'real', 'score': 0.93}]  -> interpret 'fake' vs 'real' with returned score

# For GPU inference, set device=0 (requires torch + CUDA):
# classifier = pipeline("image-classification", model="prithivMLmods/deepfake-detector-model-v1", device=0)