Deep-Fake-Detector-v2-Model

Model overview

The **Deep-Fake-Detector-v2-Model** is a state-of-the-art deep learning model designed to detect deepfake images. It leverages the **Vision Transformer (ViT)** architecture, specifically the `google/vit-base-patch16-224-in21k` model, fine-tuned on a dataset of real and deepfake images. The model is trained to classify images as either "Realism" or "Deepfake" with high accuracy, making it a powerful tool for detecting manipulated media. ** Update : ** The previous model checkpoint was obtained using a smaller classification dataset. Although it performed well in evaluation scores, its real-time performance was average due to limited variations in the training set. The new update includes a larger dataset to improve the detection of fake images. The model is based on the **Vision Transformer (ViT)**, which treats images as sequences of patches and applies a transformer encoder to learn spatial relationships. Key components include: - **Patch Embedding**: Divides the input image into fixed-size patches (16x16 pixels). - **Transformer Encoder**: Processes patch embeddings using multi-head self-attention mechanisms. - **Classification Head**: A fully connected layer for binary classification.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。