Recent Advancements In The Field Of Deepfake Detection
- URL: http://arxiv.org/abs/2308.05563v1
- Date: Thu, 10 Aug 2023 13:24:27 GMT
- Title: Recent Advancements In The Field Of Deepfake Detection
- Authors: Natalie Krueger, Dr. Mounika Vanamala, Dr. Rushit Dave
- Abstract summary: A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else.
Deepfakes have the potential to cause a variety of problems and are often used maliciously.
Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A deepfake is a photo or video of a person whose image has been digitally
altered or partially replaced with an image of someone else. Deepfakes have the
potential to cause a variety of problems and are often used maliciously. A
common usage is altering videos of prominent political figures and celebrities.
These deepfakes can portray them making offensive, problematic, and/or untrue
statements. Current deepfakes can be very realistic, and when used in this way,
can spread panic and even influence elections and political opinions. There are
many deepfake detection strategies currently in use but finding the most
comprehensive and universal method is critical. So, in this survey we will
address the problems of malicious deepfake creation and the lack of universal
deepfake detection methods. Our objective is to survey and analyze a variety of
current methods and advances in the field of deepfake detection.
Related papers
- Deepfake detection in videos with multiple faces using geometric-fakeness features [79.16635054977068]
Deepfakes of victims or public figures can be used by fraudsters for blackmailing, extorsion and financial fraud.
In our research we propose to use geometric-fakeness features (GFF) that characterize a dynamic degree of a face presence in a video.
We employ our approach to analyze videos with multiple faces that are simultaneously present in a video.
arXiv Detail & Related papers (2024-10-10T13:10:34Z) - Detecting Deepfakes Without Seeing Any [43.113936505905336]
"fact checking" is adapted from fake news detection to detect zero-day deepfake attacks.
FACTOR is a recipe for deepfake fact checking and demonstrates its power in critical attack settings.
Although it is training-free, relies exclusively on off-the-shelf features, is very easy to implement, and does not see any deepfakes.
arXiv Detail & Related papers (2023-11-02T17:59:31Z) - Comparative Analysis of Deep-Fake Algorithms [0.0]
Deepfakes, also known as deep learning-based fake videos, have become a major concern in recent years.
These deepfake videos can be used for malicious purposes such as spreading misinformation, impersonating individuals, and creating fake news.
Deepfake detection technologies use various approaches such as facial recognition, motion analysis, and audio-visual synchronization.
arXiv Detail & Related papers (2023-09-06T18:17:47Z) - Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake
Detection [58.1263969438364]
We propose adversarial head turn (AdvHeat) as the first attempt at 3D adversarial face views against deepfake detectors.
Experiments validate the vulnerability of various detectors to AdvHeat in realistic, black-box scenarios.
Additional analyses demonstrate that AdvHeat is better than conventional attacks on both the cross-detector transferability and robustness to defenses.
arXiv Detail & Related papers (2023-09-03T07:01:34Z) - Hybrid Deepfake Detection Utilizing MLP and LSTM [0.0]
A deepfake is an invention that has come with the latest technological advancements.
In this paper, we propose a new deepfake detection schema utilizing two deep learning algorithms.
We evaluate our model using a dataset named 140k Real and Fake Faces to detect images altered by a deepfake with accuracies achieved as high as 74.7%.
arXiv Detail & Related papers (2023-04-21T16:38:26Z) - DeePhy: On Deepfake Phylogeny [58.01631614114075]
DeePhy is a novel Deepfake Phylogeny dataset which consists of 5040 deepfake videos generated using three different generation techniques.
We present the benchmark on DeePhy dataset using six deepfake detection algorithms.
arXiv Detail & Related papers (2022-09-19T15:30:33Z) - Using Deep Learning to Detecting Deepfakes [0.0]
Deepfakes are videos or images that replace one persons face with another computer-generated face, often a more recognizable person in society.
To combat this online threat, researchers have developed models that are designed to detect deepfakes.
This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat.
arXiv Detail & Related papers (2022-07-27T17:05:16Z) - Watch Those Words: Video Falsification Detection Using Word-Conditioned
Facial Motion [82.06128362686445]
We propose a multi-modal semantic forensic approach to handle both cheapfakes and visually persuasive deepfakes.
We leverage the idea of attribution to learn person-specific biometric patterns that distinguish a given speaker from others.
Unlike existing person-specific approaches, our method is also effective against attacks that focus on lip manipulation.
arXiv Detail & Related papers (2021-12-21T01:57:04Z) - Deep Fake Detection: Survey of Facial Manipulation Detection Solutions [0.0]
We analyze several states of the art neural networks (MesoNet, ResNet-50, VGG-19, and Xception Net) and compare them against each other.
We find an optimal solution for various scenarios like real-time deep fake detection to be deployed in online social media platforms.
arXiv Detail & Related papers (2021-06-23T18:08:07Z) - WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection [82.42495493102805]
We introduce a new dataset WildDeepfake which consists of 7,314 face sequences extracted from 707 deepfake videos collected completely from the internet.
We conduct a systematic evaluation of a set of baseline detection networks on both existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a more challenging dataset, where the detection performance can decrease drastically.
arXiv Detail & Related papers (2021-01-05T11:10:32Z)
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.