AI生成コンテンツを正確に検出します。

Image

Real: 0.03%

Fake: 99.96%

Conclusion: FAKE

Most Accurate DeepFake Detection

99% AI Content Detection Accuracy

Drop it here or

Choose Files

Image: JPEG, PNG, WEBP ;
Video: MP4, MOV

ヒント

AI-powered deepfake detection for images, videos, and audio. A versatile solution for various applications.

Our advanced technology thoroughly examines all forms of media for authenticity, making it ideal for media companies, social media platforms, and law enforcement. Ensure credibility, maintain integrity, and verify evidence with our accurate and reliable solution against the threat of deepfakes.

Select the Image
You Believe Is AI-Generated

Product Features

Utilizes advanced machine learning algorithms to identify subtle inconsistencies and manipulations, providing precise and reliable results. This helps maintain the integrity and authenticity of your content.

Real-Time Detection

Delivers detection results within seconds, suitable for live streaming and rapid response needs. Allows immediate action against deepfake content, enhancing credibility and trust.

Batch Processing

Handles multiple files simultaneously, efficiently processing large datasets. Supports drag-and-drop and bulk selection, providing detailed reports for each file to meet high-volume needs.

Multi-Format Support

Compatible with various file types, including JPEG, PNG, WEBP, MP4 and MOV. Ensures effective analysis across different formats, making it convenient for diverse content detection requirements.

Usage Scenario

メディアとニュース

ソーシャルメディアプラットフォーム

法務および法執行機関

企業ブランドの保護

金融サービス

Application scenarios

公開されたコンテンツの真正性を保証し、誤情報の拡散を防止します。

Content

ニュース機関は、公開前にビデオの真正性を確認するためにディープフェイク検出を使用しています。

Our Technology

IDENTIFY is an innovative approach designed to distinguish between real and AI-generated images through advanced spectral and spatial analysis techniques. The method incorporates a multi-step process:

Input RGB images are converted to the YCbCr color space to separate luminance and chrominance information, essential for extracting detailed features.

Each channel (Y, Cb, Cr) undergoes FFT to transform the image into the frequency domain, revealing underlying patterns crucial for detection.

This step analyzes the spectral information by integrating the FFT results radially, capturing unique frequency patterns that differ between real and fake images.

The FFT-transformed images are split into four quadrants, and features are extracted from each, combined, and averaged. These features are then fed into a convolutional neural network (CNN) to extract critical spatial features.

The CNN integrates spatial features from all channels and passes them through a fully connected layer to classify the image as real or AI-generated.

Experimental Results

We have performed extensive training and hyperparameter tuning, such as comparing different EfficientNet models, number of convolution layers, weights, data augmentations, dropout rates, and regularizers. In the end, the following settings give us the best results:

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