Face-Obfuscated Deepfake Detection by Learning Spatial and High-Frequency Generative Artifacts

• João Rabello Alvim ICTi
• Miguel D. S. Wanderley ICTi
• Italo Duarte Oliveira ICTi
• Itallo Dias ICTi
• Matheus Paz Gamba ICTi
• Kátia Poloni ICTi

Resumo

This paper presents a study of artifacts left in images by the generative process of facial deepfakes. We hypothesize that, even when facial regions are blurred or erased to protect privacy and biometric data, an image can still be classified as fake. To address such privacy-critical scenarios, our framework is specifically designed to reliably detect facial deepfakes, even when all facial regions are deliberately obfuscated to preserve individual privacy. Our approach is grounded in the observation that spatial artifacts, high-frequency components, and region-localized features in both the pixel and transformed domains serve as strong evidence of deepfake forgeries. To capture these cues, we extract three representations of each image - the raw pixel domain, wavelet based Multi-Resolution Analysis (MRA), and the Short-Time Fourier Transform (STFT) - and process them using a state-of-the-art pre-trained feature extractor, the as OpenAI’s CLIP embedding model. The generated embeddings are used to train two different classification methods (Logistic Regression and Multi-Layer Perceptron based) and we verify if the extra information presented to the feature extractor (STFT and MRA) improves deepfake identification metrics. Our contribution can be seen as a systematic approach to enhance deepfake classifier accuracies in information deprived scenarios, where the identity of a given subject is hidden by erasing or distorting its facial landmarks. We evaluate our framework on the DFFD deepfake dataset with some pre-processing of the database (to compute the transforms and hide facial identity) and we demonstrate promising detection performance under these conditions.