Fake Image Classification via Frequency Domain Artifact Removal
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Solution Overview
Problem
Current deepfake image detection technologies face performance deterioration when the forgery method differs from the training data in image category, generation model, or brightness properties, leading to a generalization issue, particularly in frequency-based detection.
Innovation Solution
A method and apparatus utilizing a generative adversarial network (GAN) with an artifact remover and determiner to classify fake images by removing artifacts in the frequency domain and determining whether the resulting image is real or fake, trained with adversarial and normalized loss functions to preserve real images and minimize fake image artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequency-based detection is used to reduce dependence on training data, then detection performance stability is improved, but detection performance still deteriorates when forgery method differs from training data in image category, generation model, and brightness property
Solution Approach 1:
The patent segments the detection task into two independent stages: artifact removal (using a neural network that processes images in the frequency domain) and classification (using a determiner that analyzes the artifact-removed image). This segmentation allows each stage to specialize, with the artifact removal stage handling frequency-domain processing for stability and the classification stage handling generalization across different forgery methods.
Solution Approach 2:
The patent introduces an intermediary component called the artifact removal module that acts as a mediator between the input image and the classification decision. This intermediary processes the image through frequency-domain artifact removal, creating a transformed representation that is then fed to the determiner for classification, thereby separating the stability-providing frequency processing from the generalization-requiring classification task.
2Measurement precision
If image-based detection is used to learn pixel level artifacts, then detection accuracy is improved, but detection performance deteriorates rapidly when training data domain differs from test data
Solution Approach 1:
The patent changes the fundamental parameter of image processing from the image domain (pixel-level) to the frequency domain. By converting images to the frequency domain before artifact removal, the system captures artifacts in a domain that is more invariant to changes in image category, generation model, and brightness properties, thereby achieving both accuracy and robustness simultaneously.
Data Source
AI summary
An apparatus for classifying fake images according to an embodiment of the present disclosure includes an artifact remover configured to receive an input image to generate an artifact-removed image from which artifacts are removed, an artifact image generator configured to generate an artifact image by using a difference between the input image and the artifact removal image, and a determiner configured to determine whether the artifact image is a real image or a fake image by receiving the artifact image.