Improving DeepFake Detection Using Dynamic Face Augmentation
- URL: http://arxiv.org/abs/2102.09603v1
- Date: Thu, 18 Feb 2021 20:25:45 GMT
- Title: Improving DeepFake Detection Using Dynamic Face Augmentation
- Authors: Sowmen Das, Arup Datta, Md. Saiful Islam, Md. Ruhul Amin
- Abstract summary: Most publicly available DeepFake detection datasets have limited variations.
Deep neural networks tend to overfit to the facial features instead of learning to detect manipulation features of DeepFake content.
We introduce Face-Cutout, a data augmentation method for training Convolutional Neural Networks (CNN) to improve DeepFake detection.
- Score: 0.8793721044482612
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The creation of altered and manipulated faces has become more common due to
the improvement of DeepFake generation methods. Simultaneously, we have seen
detection models' development for differentiating between a manipulated and
original face from image or video content. We have observed that most publicly
available DeepFake detection datasets have limited variations, where a single
face is used in many videos, resulting in an oversampled training dataset. Due
to this, deep neural networks tend to overfit to the facial features instead of
learning to detect manipulation features of DeepFake content. As a result, most
detection architectures perform poorly when tested on unseen data. In this
paper, we provide a quantitative analysis to investigate this problem and
present a solution to prevent model overfitting due to the high volume of
samples generated from a small number of actors. We introduce Face-Cutout, a
data augmentation method for training Convolutional Neural Networks (CNN), to
improve DeepFake detection. In this method, training images with various
occlusions are dynamically generated using face landmark information
irrespective of orientation. Unlike other general-purpose augmentation methods,
it focuses on the facial information that is crucial for DeepFake detection.
Our method achieves a reduction in LogLoss of 15.2% to 35.3% on different
datasets, compared to other occlusion-based augmentation techniques. We show
that Face-Cutout can be easily integrated with any CNN-based recognition model
and improve detection performance.
Related papers
- 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) - Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive
Deep Learning Approach [0.0]
Deepfake technology is widely used, which has led to serious worries about the authenticity of digital media.
This study employs a resource-effective and transparent cost-sensitive deep learning method to effectively detect deepfake faces in videos.
arXiv Detail & Related papers (2023-12-17T14:57:10Z) - DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake
Detection [67.3143177137102]
Deepfake detection refers to detecting artificially generated or edited faces in images or videos.
We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
arXiv Detail & Related papers (2023-12-07T07:19:45Z) - 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) - 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) - End2End Occluded Face Recognition by Masking Corrupted Features [82.27588990277192]
State-of-the-art general face recognition models do not generalize well to occluded face images.
This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network.
Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
arXiv Detail & Related papers (2021-08-21T09:08:41Z) - 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) - The FaceChannel: A Fast & Furious Deep Neural Network for Facial
Expression Recognition [71.24825724518847]
Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train.
We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks.
We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
arXiv Detail & Related papers (2020-09-15T09:25:37Z)
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.