You Don't Need a Neural Network to Spot a Deepfake

Most detection pipelines today are black boxes — a neural network says "fake" and you just trust it. I wanted to see how far pure statistics could go. No deep learning. Just handcrafted image features and a logistic regression. The results were better than I expected. The setup Dataset: CIFAKE — ~60,000 images (real photos vs. AI-generated) Approach: Extract statistical features from each image, evaluate with two metrics: Covariance difference (Frobenius norm) — how different are the real vs. fake distributions? LDA accuracy — how well does a linear classifier separate the two classes? Results by feature family Feature Cov. Difference LDA Accuracy Noise residual 2.05 × 10³ 84.8% FFT (frequency) 6.23 × 10¹¹ 79.9% Texture (LBP + GLCM + Gabor) 1.05 × 10⁵ 76.2% Color statistics 5.23 × 10³ 73.0% DCT coefficients 4.65 × 10³ 68.2% Intensity statistics 2.61 × 10³ 64.3% Wavelet decomposition 8.99 × 10³ 63.1% Two things stand out: 1. Noise wins. At 84.8% LDA accuracy, noise residuals outperform