# Learning Second Order Local Anomaly for General Face Forgery Detection

arXiv: https://arxiv.org/abs/2209.15490 · HTML: https://mlsanity.com/paper/2209.15490/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 53 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues](https://mlsanity.com/paper/2007.09355/) (2020)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions](https://mlsanity.com/paper/2003.01826/) (2020)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [Deferred Neural Rendering: Image Synthesis using Neural Textures](https://mlsanity.com/paper/1904.12356/) (2019)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [Learning Self-Consistency for Deepfake Detection](https://mlsanity.com/paper/2012.09311/) (2020)
- [Two-branch Recurrent Network for Isolating Deepfakes in Videos](https://mlsanity.com/paper/2008.03412/) (2020)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues](https://mlsanity.com/paper/2003.06711/) (2020)
- [Local Relation Learning for Face Forgery Detection](https://mlsanity.com/paper/2105.02577/) (2021)
- [Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization](https://mlsanity.com/paper/2005.14405/) (2020)
- [Sharp Multiple Instance Learning for DeepFake Video Detection](https://mlsanity.com/paper/2008.04585/) (2020)

## Built on by (most cited first, 30 of 36)

- [TALL: Thumbnail Layout for Deepfake Video Detection](https://mlsanity.com/paper/2307.07494/) (2023)
- [Self-Supervised Video Forensics by Audio-Visual Anomaly Detection](https://mlsanity.com/paper/2301.01767/) (2023)
- [Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection](https://mlsanity.com/paper/2304.12489/) (2023)
- [SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes](https://mlsanity.com/paper/2211.11296/) (2022)
- [MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection](https://mlsanity.com/paper/2404.08452/) (2024)
- [Locate and Verify: A Two-Stream Network for Improved Deepfake Detection](https://mlsanity.com/paper/2309.11131/) (2023)
- [Learning Natural Consistency Representation for Face Forgery Video Detection](https://mlsanity.com/paper/2407.10550/) (2024)
- [DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues](https://mlsanity.com/paper/2309.09526/) (2023)
- [Controllable Guide-Space for Generalizable Face Forgery Detection](https://mlsanity.com/paper/2307.14039/) (2023)
- [Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection](https://mlsanity.com/paper/2403.10261/) (2024)
- [SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework](https://mlsanity.com/paper/2401.04364/) (2024)
- [From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes](https://mlsanity.com/paper/2504.04827/) (2025)
- [FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing](https://mlsanity.com/paper/2509.22412/) (2025)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [Masked Conditional Diffusion Model for Enhancing Deepfake Detection](https://mlsanity.com/paper/2402.00541/) (2024)
- [Real Face Foundation Representation Learning for Generalized Deepfake Detection](https://mlsanity.com/paper/2303.08439/) (2023)
- [DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation](https://mlsanity.com/paper/2312.10680/) (2023)
- [Discrepancy-Guided Reconstruction Learning for Image Forgery Detection](https://mlsanity.com/paper/2304.13349/) (2023)
- [Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture](https://mlsanity.com/paper/2408.12791/) (2024)
- [Learning Real Facial Concepts for Independent Deepfake Detection](https://mlsanity.com/paper/2505.04460/) (2025)
- [General GAN-generated image detection by data augmentation in fingerprint domain](https://mlsanity.com/paper/2212.13466/) (2022)
- [COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection](https://mlsanity.com/paper/2308.01520/) (2023)
- [Artifact Feature Purification for Cross-domain Detection of AI-generated Images](https://mlsanity.com/paper/2403.11172/) (2024)
- [Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection](https://mlsanity.com/paper/2412.01101/) (2024)
- [Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection](https://mlsanity.com/paper/2412.20156/) (2024)
- [Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning](https://mlsanity.com/paper/2308.13503/) (2023)
- [FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection](https://mlsanity.com/paper/2504.00454/) (2025)
- [Noise-Informed Diffusion-Generated Image Detection with Anomaly Attention](https://mlsanity.com/paper/2506.16743/) (2025)
- [Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection](https://mlsanity.com/paper/2405.15173/) (2024)
- [Towards Generalizable Deepfake Detection by Primary Region Regularization](https://mlsanity.com/paper/2307.12534/) (2023)

## Abstract (arXiv)

In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Order Local Anomaly (SOLA) learning module to mine anomalies in local regions using deep feature maps. SOLA first decomposes the neighborhood of local features by different directions and distances and then calculates the first and second order local anomaly maps which provide more general forgery traces for the classifier. We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial Rich Model (ASRM) is introduced to help mine subtle noise features via learnable high pass filters. With neither pixel level annotations nor external synthetic data, our method using a simple ResNet18 backbone achieves competitive performances compared with state-of-the-art works when evaluated on unseen forgeries.