The rise of robot influencers
As we speak, more and more innovative technology is produced and deployed, the rise of artificial intelligence and machine learning has led to the production and vast growth of autonomous robots. They can be used for example for cleaning or to assist with manufacturing.
Some robots are even influencers on Instagram and other social media platforms alike, earning over two hundred times more than the average british user. It is hard to deny that CGI/robot influencers are on the rise.
The third highest earner is Japanese Imma, known on Instagram as @imma.gram, she has 237,000 followers on the platform meaning she earns around £498,303 annually, which is 1307 per cent higher than the average UK salary.
Coming in at second is Noonoouri, her image on the platform is more cartoony than other robots on the list however her passion is anything but, she is an advocate for issues such as climate change,animal cruelty and equality. With 362,00 followers she definitely brings light to these global issues.
The vast amount of followers she has and the engagement she receives means she earns an average of £2,273,555 a year.
Claiming the top spot as the highest paid influencer is Lil Miquela, who has a staggering 2.5 million followers on Instagram. She has taken the popular social media platform by storm and is now moving on to taking the music industry by storm as she has recently come out with a new music video.
This vast popularity means that she earns an average of £8,960,650 a year. Her success has opened many doors for her, such as collaborations with Prada and Calvin Klein.
If Lil Miquela’s fast growth and ever expanding reach is anything to go off, it will be exciting to see what happens next with robot influencers, will many more people adopt the ideas? Or will Lil Miquela always lead the industry? Tweet us at @AI_Magazine_BC and let us know your thoughts.
Facebook Develops AI to Crackdown on Deepfakes
In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale?
Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.”
The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with.
Why Reverse Engineering?
Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient.
First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source.
How It Works
Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.
To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters.
What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’.
What Did They Find?
‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising.
Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.