# Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks

arXiv: https://arxiv.org/abs/2406.04932 · HTML: https://mlsanity.com/paper/2406.04932/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 58 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm](https://mlsanity.com/paper/1808.00278/) (2018)
- [ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions](https://mlsanity.com/paper/2003.03488/) (2020)
- [Forward and Backward Information Retention for Accurate Binary Neural Networks](https://mlsanity.com/paper/1909.10788/) (2019)
- [Towards Accurate Binary Convolutional Neural Network](https://mlsanity.com/paper/1711.11294/) (2017)
- [FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals](https://mlsanity.com/paper/1901.02212/) (2019)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection](https://mlsanity.com/paper/1812.02510/) (2018)

## Built on by (most cited first, 27 of 27)

- [A Bias-Free Training Paradigm for More General AI-generated Image Detection](https://mlsanity.com/paper/2412.17671/) (2024)
- [Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios](https://mlsanity.com/paper/2509.09172/) (2025)
- [LOTA: Bit-Planes Guided AI-Generated Image Detection](https://mlsanity.com/paper/2510.14230/) (2025)
- [Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook](https://mlsanity.com/paper/2411.19537/) (2024)
- [MAVOS-DD: Multilingual Audio-Video Open-Set Deepfake Detection Benchmark](https://mlsanity.com/paper/2505.11109/) (2025)
- [HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection](https://mlsanity.com/paper/2501.05631/) (2025)
- [FREAK: Frequency-modulated High-fidelity and Real-time Audio-driven Talking Portrait Synthesis](https://mlsanity.com/paper/2503.04067/) (2025)
- [Lightweight Deepfake Detection Based on Multi-Feature Fusion](https://mlsanity.com/paper/2502.11763/) (2025)
- [A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection](https://mlsanity.com/paper/2508.20449/) (2025)
- [Rethinking the Use of Vision Transformers for AI-Generated Image Detection](https://mlsanity.com/paper/2512.04969/) (2025)
- [A Difference-in-Difference Approach to Detecting AI-Generated Images](https://mlsanity.com/paper/2602.23732/) (2026)
- [GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection](https://mlsanity.com/paper/2509.10250/) (2025)
- [Phase4DFD: Multi-Domain Phase-Aware Attention for Deepfake Detection](https://mlsanity.com/paper/2601.05861/) (2026)
- [Fact or Fake? Assessing the Role of Deepfake Detectors in Multimodal Misinformation Detection](https://mlsanity.com/paper/2602.01854/) (2026)
- [CycleBNN: Cyclic Precision Training in Binary Neural Networks](https://mlsanity.com/paper/2410.00050/) (2024)
- [VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems](https://mlsanity.com/paper/2507.05146/) (2025)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [Addressing Deepfake Issue in Selfie banking through camera based authentication](https://mlsanity.com/paper/2508.19714/) (2025)
- [Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization](https://mlsanity.com/paper/2509.07993/) (2025)
- [SFANet: Spatial-Frequency Attention Network for Deepfake Detection](https://mlsanity.com/paper/2510.04630/) (2025)
- [Explainable Detection of AI-Generated Images with Artifact Localization Using Faster-Than-Lies and Vision-Language Models for Edge Devices](https://mlsanity.com/paper/2510.23775/) (2025)
- [SpectraNet: FFT-assisted Deep Learning Classifier for Deepfake Face Detection](https://mlsanity.com/paper/2511.19187/) (2025)
- [SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection](https://mlsanity.com/paper/2605.08226/) (2026)
- [Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks](https://mlsanity.com/paper/2605.17573/) (2026)
- [Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection](https://mlsanity.com/paper/2605.19360/) (2026)
- [Cross-Domain Generalization Limits of Vision Foundation Models in Facial Deepfake Detection](https://mlsanity.com/paper/2605.24965/) (2026)
- [Toward Calibrated, Fair, and accurate Deepfake Detection](https://mlsanity.com/paper/2606.09881/) (2026)

## Abstract (arXiv)

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method's state-of-the-art performance in most scenarios with a significant efficiency gain of up to a 20× reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection.