cs.CVMar 27, 2026

Face2Parts: Exploring Coarse-to-Fine Inter-Regional Facial Dependencies for Generalized Deepfake Detection

Authors: Kutub Uddin, Nusrat Tasnim, Byung Tae Oh

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Abstract

Multimedia data, particularly images and videos, is integral to various applications, including surveillance, visual interaction, biometrics, evidence gathering, and advertising. However, amateur or skilled counterfeiters can simulate them to create deepfakes, often for slanderous motives. To address this challenge, several forensic methods have been developed to ensure the authenticity of the content. The effectiveness of these methods depends on their focus, with challenges arising from the diverse nature of manipulations. In this article, we analyze existing forensic methods and observe that each method has unique strengths in detecting deepfake traces by focusing on specific facial regions, such as the frame, face, lips, eyes, or nose. Considering these insights, we propose a novel hybrid approach called Face2Parts based on hierarchical feature representation (HFRHFR) that takes advantage of coarse-to-fine information to improve deepfake detection. The proposed method involves extracting features from the frame, face, and key facial regions (i.e., lips, eyes, and nose) separately to explore the coarse-to-fine relationships. This approach enables us to capture inter-dependencies among facial regions using a channel-attention mechanism and deep triplet learning. We evaluated the proposed method on benchmark deepfake datasets in both intra-, inter-dataset, and inter-manipulation settings. The proposed method achieves an average AUC of 98.42% on FF++, 79.80% on CDF1, 85.34% on CDF2, 89.41% on DFD, 84.07% on DFDC, 95.62% on DTIM, 80.76% on PDD, and 100% on WLDR, respectively. The results demonstrate that our approach generalizes effectively and achieves promising performance to outperform the existing methods.

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Fig. 1: Key concepts of the proposed method: (a) The conventional deepfake detection, which relies solely on facial artifact analysis. (c) In contrast, the proposed method, which leverages hierarchical information across multiple levels, coarse (frame), medium (face), and fine-grained (lips, left eye, right eye, and nose), aims to improve deepfake detection performance.
Fig. 2: Categories of deepfake detection methods: (a) conventional methods, which rely on handcrafted features, (b) deep learning-based methods that automatically learn discriminative representations from data, and (c) hybrid approaches that combine traditional cues with deep learning models to improve detection performance.
Fig. 3: Examples of deepfake generation using different manipulation techniques: (a) Face swapping, where the source face is replaced with a target face in the FF++ [ 7 ] /FSW dataset, and (b) lip synchronization, where facial lip movements are manipulated using different audio tracks in the PDD [ 40 ] dataset.
Fig. 4: Performance analysis comparing feature representations: This study compares face-only and lips-only features with the proposed HFR approach. The results demonstrate that hierarchical feature representations provide superior effectiveness for deepfake detection across the FF++ [ 7 ] /FSW, FF++ [ 7 ] /NT, and PDD [ 40 ] datasets.
Fig. 5: Architecture of the proposed method: The proposed framework is composed of three sequential phases. In Phase 1, an encoder network is employed to extract hierarchical feature representations from multiple facial regions, including the full frame, face, lips, left eye, right eye, and nose. In Phase 2, a deep triplet learning strategy combined with a CA-MLP mechanism is introduced to model and exploit interrelationships among regional features, yielding discriminative embeddings organized into two clusters. Finally, in Phase 3, the learned embeddings are passed to a classifier for final prediction.
Fig. 6: Key facial regions extracted using Dlib: An 81-point facial landmark detector was employed to accurately localize facial regions and capture detailed facial information. The colored rectangles indicate the selected facial regions used for extracting coarse-to-fine-grained hierarchical features.
Fig. 7: Block diagram of the proposed CACA -MLP embedding network, which models relationships among multi-level hierarchical features extracted from the frame, face, left eye, right eye, lips, and nose regions.
Fig. 8: Illustrative examples of real and deepfake frames (from left to right) from the FF++ [ 7 ] , CDF2 [ 26 ] , DFD [ 39 ] , and PDD [ 40 ] datasets. Deepfake faces in these datasets were generated using different synthesis models.
DatasetsSubjectsFocus# of Samples (Real/Fake)ManipulationsTrain-Test
FF++ [ 7 ]977Random1000/5000FaceSwap, FaceShift, NeuralTextures3600, 700
CDF1 [ 26 ]N/ACelebrities408/795FaceSwap1027, 176
CDF2 [ 26 ]59Celebrities590/5639FaceSwap5632, 597
DFD [ 39 ]28Random363/3068Not disclosed, probably FaceSwap2744, 687
DFDC [ 39 ]66Random1131/4119Not disclosed, probably FaceSwap3675, 1575
DTIM [ 41 ]64Random320/320FaceSwap448, 192
TABLE I: Detailed overview of the dataset: Including dataset name, number of subjects, target group characteristics, sample count, and manipulation types.
Fig. 9: Deepfake detection performance across different information levels: (a) frame-level AUC and (b) video-level AUC evaluated on eight benchmark datasets.
Fig. 10: Hierarchical feature representations are extracted using a pretrained CLIP model across three levels: level-1 (frame), level-2 (face), and level-3 (lips, left eye, right eye, and nose regions).
Fig. 11: t-SNE visualization of hierarchical feature embeddings after triplet learning. The top row shows embeddings using only level-2 features, which are commonly adopted in deepfake detection, while the bottom row illustrates embeddings obtained using multi-level hierarchical features from level-1 to level-3.
Fig. 12: Performance comparison of different classification methods using Face and HFR features. (a)-(c) depict frame-level AUC, while (d)-(f) illustrate video-level AUC for FF++ [ 7 ] /DF, CDF1 [ 26 ] , and PDD [ 40 ] , respectively.
Fig. 13: Examples of failure cases in the inter-setting evaluation. (a) CDF2 [ 26 ] real, (b) CDF2 [ 26 ] fake, (c) DFD [ 39 ] real, and (d) DFD [ 39 ] fake samples. Detection results are reported using ViT-L/14, as it achieved superior performance compared to the other evaluated detectors.
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