NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images
| dc.contributor.author | Vives, Cristian | en |
| dc.contributor.committeechair | Viswanath, Bimal | en |
| dc.contributor.committeemember | Chung, Taejoong Tijay | en |
| dc.contributor.committeemember | Reddy, Chandan K. | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2022-03-22T08:00:07Z | en |
| dc.date.available | 2022-03-22T08:00:07Z | en |
| dc.date.issued | 2022-03-21 | en |
| dc.description.abstract | Recent advancements in generative models have resulted in the improvement of hyper- realistic synthetic images or "deepfakes" at high resolutions, making them almost indistin- guishable from real images from cameras. While exciting, this technology introduces room for abuse. Deepfakes have already been misused to produce pornography, political propaganda, and misinformation. The ability to produce fully synthetic content that can cause such mis- information demands for robust deepfake detection frameworks. Most deepfake detection methods are trained in a supervised manner, and fail to generalize to deepfakes produced by newer and superior generative models. More importantly, such detection methods are usually focused on detecting deepfakes having a specific type of content, e.g., face deepfakes. How- ever, other types of deepfakes are starting to emerge, e.g., deepfakes of biomedical images, satellite imagery, people, and objects shown in different settings. Taking these challenges into account, we propose NoiseLearner, an unsupervised and content-agnostic deepfake im- age detection method. NoiseLearner aims to detect any deepfake image regardless of the generative model of origin or the content of the image. We perform a comprehensive evalu- ation by testing on multiple deepfake datasets composed of different generative models and different content groups, such as faces, satellite images, landscapes, and animals. Further- more, we include more recent state-of-the-art generative models in our evaluation, such as StyleGAN3 and probabilistic denoising diffusion models (DDPM). We observe that Noise- Learner performs well on multiple datasets, achieving 96% accuracy on both StyleGAN and StyleGAN2 datasets. | en |
| dc.description.abstractgeneral | Images synthesized by artificial intelligence, commonly known as deepfakes, are starting to become indistinguishable from real images. While these technological advances are exciting with regards to what a computer can do, it is important to understand that such technol- ogy is currently being used with ill intent. Thus, identifying these images is becoming a growing necessity, especially as deepfake technology grows to perfectly mimic the nature of real images. Current deepfake detection approaches fail to detect deepfakes of other content, such as sattelite imagery or X-rays, and cannot generalize to deepfakes synthesized by new artificial intelligence. Taking these concerns into account, we propose NoiseLearner, a deep- fake detection method that can detect any deepfake regardless of the content and artificial intelligence model used to synthesize it. The key idea behind NoiseLearner is that it does not require any deepfakes to train. Instead, NoiseLearner learns the key features of real images and uses them to differentiate between deepfakes and real images – without ever looking at a single deepfake. Even with this strong constraint, NoiseLearner shows promise by detecting deepfakes of diverse contents and models used to generate them. We also explore different ways to improve NoiseLearner. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:33998 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/109381 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | deepfake | en |
| dc.subject | deepfake detection | en |
| dc.subject | generative models | en |
| dc.subject | GAN | en |
| dc.subject | artificial intelligence | en |
| dc.subject | Machine learning | en |
| dc.subject | security in Machine learning | en |
| dc.title | NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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