The DARPA Hard
Well..well…well!! We are not alone in The Journey to discover more about deepfake detections; apparently, DARPA is on it too!! YASS!! 🔥🔥
I know you might be wondering why DARPA is interested in deepfake so much!? Bcuz Deepfakes affect everyone. 🤷🏻♀️
As they say “Tell a lie often enough, and it becomes the truth”.
Well, we have all been there where we haven’t been able to judge fairly based on what we see, hear, or say anymore. untrustworthy environment.
Let’s see what DARPA has been doing for all of us & how!
Deepfakes are highly realistic and convincing artificial videos or images that have been manipulated using advanced machine-learning algorithms. They can be used to spread false information, fake news, or manipulate public opinion. Therefore, detecting deepfakes has become a crucial task in the fight against disinformation.
The Defense Advanced Research Projects Agency (DARPA) is an agency of the United States Department of Defense responsible for the development of emerging technologies for national security. One of its primary goals is to develop advanced technologies to detect and counter disinformation and propaganda, including deepfakes. DARPA has invested significant resources in this area, and it has funded several research programs aimed at developing advanced deepfake detection technologies.
AMAZING!! if that’s where our tax dollars are going… (I am partially happy!😄)
One of the most significant DARPA-funded programs in deepfake detection is the Media Forensics (MediFor) program. The MediFor program aims to develop automated tools and techniques to detect and attribute the origin of manipulated media. The program brings together researchers from academia, industry, and government agencies to develop advanced algorithms for detecting deepfakes.
The MediFor program focuses on three main areas of research: image forensics, video forensics, and audio forensics.
Image forensics involves detecting image manipulations, such as tampering, cloning, and splicing.
Video forensics focuses on detecting deepfake videos, including those created using face-swapping techniques.
Audio forensics involves detecting manipulated audio files, such as those created using voice cloning or text-to-speech technology.
To achieve its goals, the MediFor program uses a combination of machine learning, computer vision, and signal processing techniques.
Machine learning algorithms are trained on large datasets of manipulated and authentic media to learn the features and patterns of deepfakes.
Computer vision techniques are used to analyze the visual content of images and videos to detect anomalies and inconsistencies that may indicate manipulation.
Signal processing techniques are used to analyze the audio content of media to detect changes and anomalies in sound patterns.
isn’t that kool!!!? Hell yeah! 🙌🏼
In 2018, MediFor hosted a deepfake detection challenge, where participants were asked to develop algorithms to detect deepfake videos. The challenge dataset included over 5000 videos, including both deepfakes and authentic videos. The winning algorithm achieved an accuracy of 96%, demonstrating the effectiveness of advanced machine learning techniques in deepfake detection.
Guess the winning team’s pre-trained model!? Our beloved state-of-the-art EfficientNet-B7! YUHOOO!!
👻👻for your amusement 👻 👻
Winning Solution: dfdc_deepfake_challenge & ofc more info on EfficientNet.
In conclusion, DARPA’s MediFor program has achieved significant results in developing advanced algorithms for detecting deepfakes, and its research is likely to have a significant impact on the development of deepfake detection technologies in the future.
👏👏 DARPA!!! DARPA!! 👏👏
Don’t forget to catch up on the nerd brain dump! by Jasmin Bharadiya
