When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection

• URL: http://arxiv.org/abs/2209.13289v1
• Date: Tue, 27 Sep 2022 10:15:46 GMT
• Title: When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection
• Abstract summary: In this paper, we utilize and compare two kinds of handcrafted features(SIFT and HoG) and two kinds of deep features(Xception and CNN+RNN) for the deepfake detection task. We also check the performance of these features when there are mismatches between training sets and test sets.
• Score: 13.405191042154367
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The accelerated growth in synthetic visual media generation and manipulation has now reached the point of raising significant concerns and posing enormous intimidations towards society. There is an imperative need for automatic detection networks towards false digital content and avoid the spread of dangerous artificial information to contend with this threat. In this paper, we utilize and compare two kinds of handcrafted features(SIFT and HoG) and two kinds of deep features(Xception and CNN+RNN) for the deepfake detection task. We also check the performance of these features when there are mismatches between training sets and test sets. Evaluation is performed on the famous FaceForensics++ dataset, which contains four sub-datasets, Deepfakes, Face2Face, FaceSwap and NeuralTextures. The best results are from Xception, where the accuracy could surpass over 99\% when the training and test set are both from the same sub-dataset. In comparison, the results drop dramatically when the training set mismatches the test set. This phenomenon reveals the challenge of creating a universal deepfake detection system.

Related papers

• Combating Digitally Altered Images: Deepfake Detection [0.0]
  This study presents a robust Deepfake detection based on a modified Vision Transformer(ViT) model.<n>The model has been trained on a subset of the OpenForensics dataset with multiple augmentation techniques to increase robustness for diverse image manipulations.<n>The model demonstrates state-of-the-art results on the test dataset to meticulously detect Deepfake images.
  arXiv  Detail & Related papers  (2025-08-23T09:59:03Z)
• Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks [81.21729774122554]
  Existing deepfake detection methods overlook the block effects" introduced by compression in Online Social Networks (OSNs)<n>We propose PLADA, a novel framework designed to tackle the lack of paired data and the ineffective use of compressed images.<n>PLADA consists of two core modules: Block Effect Eraser (B2E), which uses a dual-stage attention mechanism to handle block effects, and Open Data Aggregation (ODA), which processes both paired and unpaired data to improve detection.
  arXiv  Detail & Related papers  (2025-06-25T15:46:41Z)
• AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors [24.78672820633581]
  Deep generative models can create remarkably fake images while raising concerns about misinformation and copyright infringement. Deepfake detection technique is developed to distinguish between real and fake images. We propose a novel approach called AntifakePrompt, using Vision-Language Models and prompt tuning techniques.
  arXiv  Detail & Related papers  (2023-10-26T14:23:45Z)
• CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
  We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF) Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts. It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
  arXiv  Detail & Related papers  (2023-09-30T12:30:25Z)
• Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models [35.188364409869465]
  We present an investigation into how deepfakes are produced and how they can be identified. The cornerstone of our research is a rich collection of artificial celebrity faces, titled DeepFakeFace. This data serves as a robust foundation to train and test algorithms designed to spot deepfakes.
  arXiv  Detail & Related papers  (2023-09-05T13:22:41Z)
• Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors [2.0649235321315285]
  There is a dire need for deepfake detection technology to help spot deepfake media. Current deepfake detection models are able to achieve outstanding accuracy (>90%) This study identifies makeup application as an adversarial attack that could fool deepfake detectors.
  arXiv  Detail & Related papers  (2022-04-19T02:24:30Z)
• Voice-Face Homogeneity Tells Deepfake [56.334968246631725]
  Existing detection approaches contribute to exploring the specific artifacts in deepfake videos. We propose to perform the deepfake detection from an unexplored voice-face matching view. Our model obtains significantly improved performance as compared to other state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-03-04T09:08:50Z)
• Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System [0.0]
  Sparse events, such as malign attacks in real-time network traffic, have caused big organisations an immense hike in revenue loss. Standard methods used to detect intrusions are not promising and have significant failure to identify new malware. Our proposed work overcomes these challenges by giving promising results using deep-stacked autoencoders in two stages.
  arXiv  Detail & Related papers  (2021-12-03T07:59:02Z)
• An Experimental Evaluation on Deepfake Detection using Deep Face Recognition [0.0]
  Deep learning has led to the generation of very realistic fake content, also known as deepfakes. Most of the current deepfake detection methods are deemed as a binary classification problem in distinguishing authentic images or videos from fake ones using two-class convolutional neural networks (CNNs) This paper thoroughly evaluate the efficacy of deep face recognition in identifying deepfakes, using different loss functions and deepfake generation techniques.
  arXiv  Detail & Related papers  (2021-10-04T18:02:56Z)
• M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection [74.19291916812921]
  forged images generated by Deepfake techniques pose a serious threat to the trustworthiness of digital information. In this paper, we aim to capture the subtle manipulation artifacts at different scales for Deepfake detection. We introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods.
  arXiv  Detail & Related papers  (2021-04-20T05:43:44Z)
• 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)
• Adversarially robust deepfake media detection using fused convolutional neural network predictions [79.00202519223662]
  Current deepfake detection systems struggle against unseen data. We employ three different deep Convolutional Neural Network (CNN) models to classify fake and real images extracted from videos. The proposed technique outperforms state-of-the-art models with 96.5% accuracy.
  arXiv  Detail & Related papers  (2021-02-11T11:28:00Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)

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.