Why Do Facial Deepfake Detectors Fail?
- URL: http://arxiv.org/abs/2302.13156v2
- Date: Sun, 10 Sep 2023 05:47:11 GMT
- Title: Why Do Facial Deepfake Detectors Fail?
- Authors: Binh Le, Shahroz Tariq, Alsharif Abuadbba, Kristen Moore, Simon Woo
- Abstract summary: Recent advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio.
These materials pose significant challenges to human authentication, such as impersonation, misinformation, or even a threat to national security.
Several deepfake detection algorithms have been proposed, leading to an ongoing arms race between deepfake creators and deepfake detectors.
- Score: 9.60306700003662
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent rapid advancements in deepfake technology have allowed the creation of
highly realistic fake media, such as video, image, and audio. These materials
pose significant challenges to human authentication, such as impersonation,
misinformation, or even a threat to national security. To keep pace with these
rapid advancements, several deepfake detection algorithms have been proposed,
leading to an ongoing arms race between deepfake creators and deepfake
detectors. Nevertheless, these detectors are often unreliable and frequently
fail to detect deepfakes. This study highlights the challenges they face in
detecting deepfakes, including (1) the pre-processing pipeline of artifacts and
(2) the fact that generators of new, unseen deepfake samples have not been
considered when building the defense models. Our work sheds light on the need
for further research and development in this field to create more robust and
reliable detectors.
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) - Deepfake Media Forensics: State of the Art and Challenges Ahead [51.33414186878676]
AI-generated synthetic media, also called Deepfakes, have influenced so many domains, from entertainment to cybersecurity.
Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques.
This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.
arXiv Detail & Related papers (2024-08-01T08:57:47Z) - DF40: Toward Next-Generation Deepfake Detection [62.073997142001424]
existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors on one specific dataset and testing them on other prevalent deepfake datasets.
But can these stand-out "winners" be truly applied to tackle the myriad of realistic and diverse deepfakes lurking in the real world?
We construct a highly diverse deepfake detection dataset called DF40, which comprises 40 distinct deepfake techniques.
arXiv Detail & Related papers (2024-06-19T12:35:02Z) - How Generalizable are Deepfake Image Detectors? An Empirical Study [4.42204674141385]
We present the first empirical study on the generalizability of deepfake detectors.
Our study utilizes six deepfake datasets, five deepfake image detection methods, and two model augmentation approaches.
We find that detectors are learning unwanted properties specific to synthesis methods and struggling to extract discriminative features.
arXiv Detail & Related papers (2023-08-08T10:30:34Z) - Fooling State-of-the-Art Deepfake Detection with High-Quality Deepfakes [2.0883760606514934]
We show that deepfake detectors proven to generalize well on multiple research datasets still struggle in real-world scenarios with well-crafted fakes.
We propose a novel autoencoder for face swapping alongside an advanced face blending technique, which we utilize to generate 90 high-quality deepfakes.
arXiv Detail & Related papers (2023-05-09T09:08:49Z) - 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) - 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) - Understanding the Security of Deepfake Detection [23.118012417901078]
We study the security of state-of-the-art deepfake detection methods in adversarial settings.
We use two large-scale public deepfakes data sources including FaceForensics++ and Facebook Deepfake Detection Challenge.
Our results uncover multiple security limitations of the deepfake detection methods in adversarial settings.
arXiv Detail & Related papers (2021-07-05T14:18:21Z) - 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) - The Creation and Detection of Deepfakes: A Survey [32.04375809239154]
Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake.
In this paper, we explore the creation and detection of deepfakes and provide an in-depth view of how these architectures work.
arXiv Detail & Related papers (2020-04-23T13:35:49Z)
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.