Monash University and the Indian Institute of Technology Ropar have teamed up to detect and uncover deepfake videos.
A deepfake is a video that has been manipulated to show someone saying or doing something that never happened.
As technology becomes more advanced, the lines between reality and fake news are becoming increasingly blurred, leading to the spread of misinformation on crucial issues such as the current COVID-19 pandemic, or elections.
The researchers have trained machine learning algorithms to detect deepfake videos based on the dissimilarity in patterns between the audio and visual cues.
Essentially, the algorithm breaks down the video based on segments and analyses each section to produce a dissonance score based on the disharmony it has detected between the audio and visuals. This could be anything such as unnatural facial and lip movements or a lag in the audio.
As authorities and tech companies struggle to keep up with the advancements in deepfakes, this research offers a potential solution to identify manipulated videos circling the internet, the researchers state.
Project lead, Dr Abhinav Dhall from the Faculty of Information Technology (IT) at Monash University, says the dual deepfake detection approach is essential to overcoming misinformation online.
Dhall says, "The machine learning method we've developed is applying a detection technique similar to watching a foreign film with overlaid audio that is not in sync with the lip movements.
"This disharmony between the audio and the visual leads the viewer to notice that the video isn't quite right, which is what were mimicking with the machine learning algorithm."
By producing a 'dissonance score', the algorithm detects if something isn't quite right in a video and then identifies the exact part of a video that has been manipulated.
The machine learning algorithm independently learns from these discriminative features, further advancing its ability to detect future deepfakes, the researchers state.
Initial research experiments on existing deepfake datasets of more than 18,000 have shown that this particular approach has outperformed other advanced deepfake detection methods, and can correctly distinguish between real and manipulated videos with a success rate of 91.5%.
Associate Professor Ramanathan Subramanian, from the Indian Institute of Technology Ropar, explains the urgent need for advanced deepfake detection methodologies.
He says, "Deepfakes are becoming an increasingly major concern worldwide. They build upon the problems created by fake news and pose a huge potential threat to democracy.
"With the upcoming US election, AI-generated images, audio and video are increasingly affecting our ability to separate fact from fiction in the political sphere. A reliable deepfake detection algorithm is needed now more than ever."