A Note on Deepfake Detection with Low-Resources

• URL: http://arxiv.org/abs/2006.05183v1
• Date: Tue, 9 Jun 2020 11:07:08 GMT
• Title: A Note on Deepfake Detection with Low-Resources
• Authors: Piotr Kawa and Piotr Syga
• Abstract summary: Deepfakes are videos that include changes, quite often substituting face of a portrayed individual with a different face using neural networks. We present two methods that allow detecting Deepfakes for a user without significant computational power.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deepfakes are videos that include changes, quite often substituting face of a portrayed individual with a different face using neural networks. Even though the technology gained its popularity as a carrier of jokes and parodies it raises a serious threat to ones security - via biometric impersonation or besmearing. In this paper we present two methods that allow detecting Deepfakes for a user without significant computational power. In particular, we enhance MesoNet by replacing the original activation functions allowing a nearly 1% improvement as well as increasing the consistency of the results. Moreover, we introduced and verified a new activation function - Pish that at the cost of slight time overhead allows even higher consistency. Additionally, we present a preliminary results of Deepfake detection method based on Local Feature Descriptors (LFD), that allows setting up the system even faster and without resorting to GPU computation. Our method achieved Equal Error Rate of 0.28, with both accuracy and recall exceeding 0.7.

Related papers

• Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes [3.6308756891251392]
  Real-time deepfake, a type of generative AI, is capable of "creating" non-existing contents (e.g., swapping one's face with another) in a video. It has been misused to produce deepfake videos for malicious purposes, including financial scams and political misinformation. We propose SFake, a new real-time deepfake detection method that exploits deepfake models' inability to adapt to physical interference.
  arXiv  Detail & Related papers  (2024-09-17T04:58:30Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Real-Time Deepfake Detection in the Real-World [39.96935319559675]
  This paper introduces "Locally Aware Deepfake Detection" Algorithm (LaDeDa) LaDeDa accepts a single 9x9 image patch and outputs its deepfake score. We introduce WildRF, a new deepfake detection dataset curated from several popular social networks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:23Z)
• Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks [0.0]
  Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. We introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss.
  arXiv  Detail & Related papers  (2024-06-07T13:37:36Z)
• Facial Forgery-based Deepfake Detection using Fine-Grained Features [7.378937711027777]
  Facial forgery by deepfakes has caused major security risks and raised severe societal concerns. We formulate deepfake detection as a fine-grained classification problem and propose a new fine-grained solution to it. Our method is based on learning subtle and generalizable features by effectively suppressing background noise and learning discriminative features at various scales for deepfake detection.
  arXiv  Detail & Related papers  (2023-10-10T21:30:05Z)
• SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning [55.84746218227712]
  We develop SqueezerFaceNet, a light face recognition network which less than 1M parameters. We show that it can be further reduced (up to 40%) without an appreciable loss in performance.
  arXiv  Detail & Related papers  (2023-07-20T08:38:50Z)
• 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)
• Self-supervised Transformer for Deepfake Detection [112.81127845409002]
  Deepfake techniques in real-world scenarios require stronger generalization abilities of face forgery detectors. Inspired by transfer learning, neural networks pre-trained on other large-scale face-related tasks may provide useful features for deepfake detection. In this paper, we propose a self-supervised transformer based audio-visual contrastive learning method.
  arXiv  Detail & Related papers  (2022-03-02T17:44:40Z)
• Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features [13.033517345182728]
  Deepfakes is a branch of malicious techniques that transplant a target face to the original one in videos. Previous efforts for Deepfakes videos detection mainly focused on appearance features, which have a risk of being bypassed by sophisticated manipulation. We propose an efficient and robust framework named LRNet for detecting Deepfakes videos through temporal modeling on precise geometric features.
  arXiv  Detail & Related papers  (2021-04-09T16:57:55Z)
• Multi-attentional Deepfake Detection [79.80308897734491]
  Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. We propose a new multi-attentional deepfake detection network. Specifically, it consists of three key components: 1) multiple spatial attention heads to make the network attend to different local parts; 2) textural feature enhancement block to zoom in the subtle artifacts in shallow features; 3) aggregate the low-level textural feature and high-level semantic features guided by the attention maps.
  arXiv  Detail & Related papers  (2021-03-03T13:56:14Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)

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.