Author: Jason Chiou Nong Dai

When we were discussing different importance of links and how we can connect two nodes together through decentralized search, I thought of how we can be matched to other people depending on certain factors, and a big one that came to mind was matchmaking in video games. There are two major factors that go into this matching; how good the network connection is between two players (which is also often affected by location, i.e. physical distance apart), and skill disparity between the two players.

To do this, usually the skill level of each player is targeted first, and the system will look for the player in closest proximity. In a paper on matchmaking strategies, they took the example of League of Legends, a popular online multiplayer game with millions of players worldwide, and analysed the way the grouped players together to be in a match. In this case, the main factor was the ranking of each player, and they would match to the closest players with similar ranking, broadening the scope of the search and the allowed difference in ranking as time spent waiting for the queue increased.

One of problems brought up in the article is that this matchmaking does not take into account latency, which would be part the network connections strength, and affected by physical distance between players. It suggests that to handle a multiple criteria search on such a large scale, decentralized architecture is required.

This is where something like decentralized search from our lectures could be used. Something like this already exists for player matchmaking in games, and is called peer-to-peer matchmaking. Peer-to-peer to put is simply, is a decentralized communication model, and in this context would be two players (nodes) communicating between each other to play a game, rather than a centralized system dealing with hosting servers and networks.

This network is the same as a Kleinberg model, and through the game, each player is given the matchmaking strategy to connect with other players in the P2P network, and can navigate through this searchable network for another player. Although this system still has it’s flaws, it allows players to quickly connect to others, and is an interesting example of the applications of a decentralized search and the analysis of large networks in the context of a popular video game.

Articles: https://www.researchgate.net/publication/260086948_Matchmaking_in_multiplayer_online_games_Studying_user_traces_to_improve_the_user_experience

https://link.springer.com/article/10.1007/s12083-019-00725-3#Sec3

An interesting application of machine learning and artificial intelligence that has become easily accessible and usable in recent years has been deepfakes, which are alterations of visual and audio elements in a video to create realistic and possibly indiscernable new videos which often overlay a new persona onto a scene. Deepfakes have become very popular in internet culture, with the use of it in memes which take popular celebrities and media figures images and make them sing the song “Baka Mitai”.

I’m sure most of you have seen one of these memes if you browse social media, and when I first saw these memes, it got me interested in how it was possible to make a historical figure who has been deceased for decades to sing a song from a game made in the 21st century.

Upon looking deeper into this subject, I found that this had a lot of possible applications, many of which were negative, such as identity/financial fraud, which has made this a dangerous technique that requires monitoring and limitations. For this new wave of deepfakes, they make use of a recently published technique from a 2019 paper, which only requires a single image, and utilizes a dense motion network in determining the changes needed to be made to the image, as well a generation network for creating the new video.

These networks are made through the encoding and decoding of the keypoint layers, in what is called an autoencoder, and is represented in the same way as the graphs of networks we have been learning in class.

You can see how these networks are represented by a directed graph, with keypoints being the nodes, and edges representing connections to keypoints of other keypoint layers in the network. This is a simplified version, but represents what the dense motion network and generation network are doing, by encoding and decoding the keypoints in the graph, they can identify the keypoints that need to be modified to create the desired output motion on the input image. Another interesting thing I noticed about this graph is that it is a directed acyclic graph (DAG), which makes sense as the algorithm should not need to go back to a previous frame in the video it has already modified.

It is interesting how such a complex application can be abstracted down to where anyone can use it, and has resulted in the use for such mundane things as internet humour, despite the much more sinister uses it could and likely has already been used for. As this technology continues to advance, it could many security issues, but I don’t think that should stop us from developing it further and spreading this knowledge through dumb memes. I encourage you to try it for yourself, as it is extremely easy to try it out and you might learn something new, as I have only very briefly outlined the basics of how this works.

Article: https://www.technologyreview.com/2020/08/28/1007746/ai-deepfakes-memes/

About the Algorithm:

https://aliaksandrsiarohin.github.io/first-order-model-website/