GenAI Disinformation Mitigation Strategies and Global Competition

Highlighted the dangers of Dis/Mis information and Deepfakes around 2 years back. The Blogpost includes resources from US Department of Defense , NATO and German Government – offset AI Powered Disinfo deepfakes and Augment AI as an effective credible Countermeasure to deter future incidents in its tracks.

Deepfake Threats and Countermeasures

Artificial intelligence (AI) can play a crucial role in combating the spread and impact of deepfakes by employing various techniques to detect, prevent, and mitigate the harm caused by these manipulated media. Here are some specific ways in which AI can serve as a countermeasure against deepfakes:

1. Deepfake Detection:

AI-powered deepfake detection tools can analyze audio, video, and text content to identify subtle anomalies and patterns that are indicative of manipulation. These tools employ machine learning algorithms trained on large datasets of real and fake media to learn the telltale signs of deepfakes, such as inconsistencies in facial expressions, skin texture, lighting, and audio synchronization.

2. Content Authentication:

AI-based content authentication techniques can embed digital fingerprints or watermarks into media content, creating a unique identifier that can be used to verify the authenticity of the content. These watermarks are designed to be resilient to manipulation and can be detected even if the content has been altered.

3. Media Provenance Verification:

AI algorithms can analyze the metadata and history of media content to track its origins and identify potential sources of manipulation. This can help to establish the authenticity of content and identify potential deepfakes that have been circulating online.

4. Social Media Monitoring:

AI-powered social media monitoring tools can scan social media platforms for potentially harmful deepfakes, identifying and flagging content that is likely to spread misinformation or cause harm. This can help to limit the reach of deepfakes and prevent them from causing widespread damage.

5. Public Awareness Campaigns:

AI-based tools can be used to create personalized and targeted public awareness campaigns that educate people about deepfakes and how to identify them. These campaigns can help to reduce the susceptibility of individuals to deepfake manipulation and promote critical thinking in the digital age.

In addition to these specific applications, AI can also contribute to the development of more sophisticated deepfake detection and prevention methods. As AI research continues to advance, we can expect to see even more innovative and effective ways to combat deepfakes and protect against their harmful effects.

Here’s a list of some open-source (FOSS) tools to detect deepfakes:

1. Deepstar: Developed by ZeroFox, Deepstar is an open-source toolkit that provides a suite of tools for detecting, analyzing, and mitigating deepfakes. It includes a curated library of deepfake and real videos, a plug-in framework for testing and comparing different detection algorithms, and code for aiding in the creation of deepfake datasets.
2. FakeFinder: Created by IQT Labs, FakeFinder is an open-source framework that utilizes a combination of deep learning models and traditional image processing techniques to detect deepfakes. It aims to provide a comprehensive and robust solution for identifying manipulated media.
3. DeepSafe: Developed by Siddharth Sah, DeepSafe is an open-source deepfake detection platform that aggregates various detection models and provides a dashboard for visualizing and analyzing results. It also facilitates the creation of a dataset of potentially harmful deepfakes for further research and improvement of detection methods.
4. Visual DeepFake Detection: This open-source tool takes a different approach to deepfake detection, focusing on identifying anomalies in facial expressions, eye movements, and skin texture. It utilizes a combination of traditional image analysis techniques and machine learning to detect subtle signs of manipulation.
5. FALdetector: This open-source tool in Python is designed to detect Photoshopped faces by analyzing image metadata and identifying inconsistencies in lighting, shadows, and color patterns. It primarily focuses on detecting manipulations in still images rather than videos.
6. Deepware Scanner: Developed by Deepware, Deepware Scanner is an open-source forensic tool that analyzes videos for signs of deepfaking. It employs a deep learning model trained on a large dataset of real and fake videos to identify anomalies and inconsistencies.
7. Reality Defender: Developed by Sensity, Reality Defender is an open-source deepfake detection tool that utilizes a combination of machine learning and image processing techniques to identify manipulated media. It is designed to be easily integrated into existing applications and workflows.
8. DFV: Developed by the University of California, Berkeley, DFV is an open-source deepfake detection framework that utilizes a combination of spatial and temporal features to identify manipulated videos. It is designed to be lightweight and efficient, making it suitable for real-time applications.
9. DeepFake-o-Meter: This open-source tool provides a web-based interface for detecting deepfakes. It utilizes a combination of machine learning algorithms to analyze videos and provide a probability score indicating the likelihood of manipulation.
10. Open Video Prediction Model (OVPM): Developed by Facebook AI Research, OVPM is an open-source deep learning model for detecting deepfakes. It is trained on a large dataset of real and fake videos and can be used to identify manipulated videos with high accuracy.

These open-source tools represent a growing effort to combat the spread of deepfakes and protect against their harmful effects. As research continues to advance, we can expect to see even more sophisticated and effective FOSS tools emerge in the future.

https://www.linkedin.com/posts/danish-khan-easytech4all_deepfake-media-artificialintelligence-activity-6771673460205776896-1doe

NATO’s Approach to Countering Disinformation

https://www.nato.int/cps/en/natohq/topics_219728.htm?selectedLocale=en

US Department of Defense Approach – Deepfake Disinfo Countermeasures

https://drive.google.com/file/d/1Jpd1yXGk12jixN7Fln-kc0ewOhbgl0KW/view?usp=drivesdk

German Government’s approach to countering disinformation and Deepfakes

https://www.bsi.bund.de/EN/Themen/Unternehmen-und-Organisationen/Informationen-und-Empfehlungen/Kuenstliche-Intelligenz/Deepfakes/deepfakes_node.html

Misc Resources

https://www.scirp.org/journal/paperinformation.aspx?paperid=112520

https://www.semanticscholar.org/paper/DEEPFAKES%3A-THREATS-AND-COUNTERMEASURES-SYSTEMATIC-Albahar-Almalki/cd1cbbe9b7e5cb47c9f3aaf1b475d4694d9b2492

https://link.springer.com/article/10.1007/s10489-022-03766-z

https://www.socialproofsecurity.com/

Meet Rachel Tobac – Social Engineering and Disinformation Countermeasures.

2 years ago on stage I was asked “when will Deepfake video/audio impact trust & be believable in social engineering?” My response then was that we were 2 years away from undetectable Deepfakes. I wish my prediction then was wrong. We need synthetic media detection + labels ASAP. pic.twitter.com/yUUOTDepYY

— Rachel Tobac (@RachelTobac) February 26, 2021

Download Countersocial

CounterSocial – A Next-Gen Social Network

A unique social network. No trolls. No abuse. No ads. No fake-news. No foreign influence operations.

CounterSocial is the first Social Network Platform to take a zero-tolerance stance to hostile nations, bot accounts, trolls and disinformation networks who are weaponizing OUR own social media platforms and freedoms to engage in influence operations against us.

And we’re here to counter it.

CounterSocial is 100% crowd powered by it’s users, and does not run ads or ‘promoted content’. Our users data is not mined or sold for any purpose.

https://play.google.com/store/apps/details?id=counter.social.android