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A comprehensive AI image detection project that combines four technologies—PRNU noise analysis, ELA error level analysis, frequency domain feature extraction, and metadata detection—to accurately distinguish between real photos and AI-generated images using three independent neural network models.
Section 01
A comprehensive AI image detection project that combines four technologies—PRNU noise analysis, ELA error level analysis, frequency domain feature extraction, and metadata detection—to accurately distinguish between real photos and AI-generated images using three independent neural network models.
Section 02
Section 03
The advancement of generative AI image technology has brought risks of 'Deepfake' and misinformation spread. Traditional manual review is inefficient and struggles to handle massive content. Therefore, developing an automated AI image detection system has become an urgent need.
However, AI image detection faces many challenges:
A single detection method is difficult to handle these complex situations; a multi-dimensional, complementary detection strategy is needed.
Section 04
This project adopts a multi-module fusion architecture, combining four complementary analysis technologies:
| Module | Technology | Detection Dimension |
|---|---|---|
| PRNU | Photo Response Non-Uniformity Noise | Camera Sensor Fingerprint |
| ELA | Error Level Analysis | JPEG Compression Artifacts |
| FREQ | Frequency Domain Analysis | FFT/DCT Features |
| Metadata | Metadata Parsing | EXIF Information and Tool Signatures |
The four modules run independently, and the final comprehensive judgment result is obtained by fusing the scores of each module through weighted average.
Section 05
PRNU (Photo Response Non-Uniformity) is an inherent characteristic of digital camera sensors. Each pixel has slight differences in light response, forming a unique 'sensor fingerprint'. Real photos must carry the PRNU features of the shooting device, while AI-generated images do not have this physical-level noise pattern.
PRNU Analysis Process:
This method is very effective for detecting fully synthetic AI images, but may be affected by re-compression and geometric transformations.
Section 06
ELA (Error Level Analysis) is a classic technology for detecting image tampering. Its principle is: re-compress the image, compare it with the original image, and calculate the error level of each pixel. The error distribution of real images is usually relatively uniform, while tampered areas (including AI-generated areas) show abnormal error characteristics.
For AI-generated images, ELA can detect:
Section 07
Frequency domain analysis converts images to the frequency domain through Fourier Transform (FFT) and Discrete Cosine Transform (DCT) to analyze their frequency distribution characteristics. Studies show that AI-generated images often exhibit different patterns in the frequency domain compared to real photos:
Section 08
Metadata detection makes judgments by analyzing the EXIF information and file structure characteristics of images:
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