Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features

• URL: http://arxiv.org/abs/2507.20608v1
• Date: Mon, 28 Jul 2025 08:19:22 GMT
• Title: Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features
• Authors: Alejandro Hinke-Navarro, Mario Nieto-Hidalgo, Juan M. Espin, Juan E. Tapia,
• Abstract summary: Deepfake and face swap technologies have raised significant concerns in digital security.<n>This study proposes an enhanced deep-learning detection framework that combines handcrafted frequency-domain features with conventional RGB inputs.
• Score: 44.45692491500845
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid advancement of deepfake and face swap technologies has raised significant concerns in digital security, particularly in identity verification and onboarding processes. Conventional detection methods often struggle to generalize against sophisticated facial manipulations. This study proposes an enhanced deep-learning detection framework that combines handcrafted frequency-domain features with conventional RGB inputs. This hybrid approach exploits frequency and spatial domain artifacts introduced during image manipulation, providing richer and more discriminative information to the classifier. Several frequency handcrafted features were evaluated, including the Steganalysis Rich Model, Discrete Cosine Transform, Error Level Analysis, Singular Value Decomposition, and Discrete Fourier Transform

Related papers

• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• Towards Generalizable Deepfake Detection with Spatial-Frequency Collaborative Learning and Hierarchical Cross-Modal Fusion [3.9408262382784236]
  We propose a novel framework that integrates multi-scale spatial-frequency analysis for universal deepfake detection.<n>Our method outperforms state-of-the-art deepfake detection methods in both accuracy and generalizability.
  arXiv  Detail & Related papers  (2025-04-24T03:23:35Z)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• Deep Convolutional Pooling Transformer for Deepfake Detection [54.10864860009834]
  We propose a deep convolutional Transformer to incorporate decisive image features both locally and globally. Specifically, we apply convolutional pooling and re-attention to enrich the extracted features and enhance efficacy. The proposed solution consistently outperforms several state-of-the-art baselines on both within- and cross-dataset experiments.
  arXiv  Detail & Related papers  (2022-09-12T15:05:41Z)
• Adaptive Frequency Learning in Two-branch Face Forgery Detection [66.91715092251258]
  We propose Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. We liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers.
  arXiv  Detail & Related papers  (2022-03-27T14:25:52Z)
• Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning [12.585152735152937]
  forgery detection has attracted more and more attention due to security concerns. Existing approaches attempt to use frequency information to mine subtle artifacts under high-quality forged faces. We propose a progressive enhancement learning framework to exploit both the RGB and fine-grained frequency clues.
  arXiv  Detail & Related papers  (2021-12-28T03:18:53Z)
• Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain [88.7339322596758]
  We present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery. SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.
  arXiv  Detail & Related papers  (2021-03-02T16:45:08Z)
• Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks [14.781702606707642]
  We propose a two-stream convolutional neural network architecture called TwoStreamNet to complement frequency and spatial domain features. The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors.
  arXiv  Detail & Related papers  (2021-01-03T18:05:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.