问题: Discriminator.py 与 Classifier.py 在深度伪造检测精度上有何区别?

Title: [Question/Discussion] Deepfake Detection Upper Bound: Why do our CNN-based detectors plateau at ~95% Accuracy on StyleGAN3 outputs?

Hello StyleGAN3 Team, First, thank you for the incredible work on this architecture. The alias-free, equivariant design is a masterclass in generative modeling. NVIDIA has invited researchers to use "fake" images generated by its StyleGAN3 to improve Real/Fake Detectors (Classifiers). https://blogs.nvidia.com/blog/how-researchers-use-nvidia-ai-to-help-mitigate-misinformation/ This [building Real/Fake Detectors (Classifiers)] is one aspect of what we are doing now. I am reaching out with a research question regarding the detectability of StyleGAN3 "fake" outputs. We are currently using a Real/Fake dataset published on Kaggle [/kaggle/input/datasets/chuneeb/deepfake-detection-dataset-2026/FINAL_DATASET.csv] and training heavily optimized Vision Transformers and CNN-hybrid models on the dataset consisting of about 6k of Real vs. StyleGAN3-generated Fake images. The dataset labels indicate that the origin of all the "fake" images is "StyleGAN3" but the images are sized at "224x224" (which is not a native output resolution of the StyleGAN models). We have built and trained several model architectures that train to a Train Accuracy of about 95% and a maximum Test Accuracy of 95.1% For example: Ep 1/12 | Train Loss: 0.189 | Test Acc: 89.0% Ep 2/12 | Train Loss: 0.178 | Test Acc: 94.9% Ep 3/12 | Train Loss: 0.170 | Test Acc: 94.9% Ep 4/12 | Train Loss: 0.159 | Test Acc: 94.9% Ep 5/12 | Train Loss: 0.157 | Test Acc: 94.5% Ep 6/12 | Train Loss: 0.159 | Test Acc: 94.9% Ep 7/12 | Train Loss: 0.162 | Test Acc: 94.5% Ep 8/12 | Train Loss: 0.152 | Test Acc: 94.7% Ep 9/12 | Train Loss: 0.164 | Test Acc: 95.1% Ep 10/12 | Train Loss: 0.153 | Test Acc: 93.2% Ep 11/12 | Train Loss: 0.157 | Test Acc: 94.7% Ep 12/12 | Train Loss: 0.165 | Test Acc: 94.9% The likely culprit (depriving us of 99% Accuracy) is StyleGAN3's alias-free architecture? Older GANs (like StyleGAN1 and 2) left behind high-frequency "checkerboard" artifacts and structural noise due to how convolutions and upsampling were mathematically applied? Deepfake detectors could easily hit 99.9% accuracy by just looking at the frequency domain? StyleGAN3 explicitly removed these artifacts. By enforcing strict translation and rotation equivariance, StyleGAN3 fakes are notoriously missing the typical "CNN fingerprints" that standard deepfake detectors rely on? Our Observation: No matter how we scale the architecture or optimize the hyperparameters, our Train and Test Accuracies firmly plateau at around 95.1%. We suppose that the dataset contains images originally generated at 1024x1024 by StyleGAN3 but then somehow scaled down to 224x224. Should we use a different real/fake dataset and re-train and expect different (Better/Worse Accuracy) results? Real/Fake Dataset recommendations? Our Questions: 1. **The