The Ultimate Guide To Being Human

Obliteration as a Service

OaaS is an article series designed to explore the realistic threats of Artificial Intelligence, with a focus on building viable defenses against them. Across the series, we will explore how existing tools and techniques can be used in exceedingly disturbing ways in order to pursue dark agendas. Each article provides a digestible description of the technical context, a blueprint of the unsettling system, and an outline of possible defense mechanisms against it.

For more details about OaaS, including the complete list of articles, visit this link.

The Context

Bot is the contracted version of robot. We generally think of robots as physical machines which operate in physical environments. Those machines perceive the world through an array of sensors, like a light sensor, and can interact with the world through an array of actuators, like a motor. The etymology of the word robot itself suggests something physical, originating from the Czech word robota, which means forced labor.

Photo by Franck V. on Unsplash

Contrary to popular opinion, robots can operate completely differently. In reality, most of them do not even exist in a physical environment, but in a virtual one. Social media platforms, like Facebook or Twitter, can serve as fully-fledged worlds in which robots can operate. Instead of perceiving the physical nature of their environment, like the intensity of light, those robots can observe the way users are interacting on the platform by reading what they post, by looking at how they react to content, or by noticing their specific virtual appearance. Consequently, instead of physically interacting with the world around them, like through a robotic arm, those robots can actively engage with users by creating new posts, by reacting to content, or by changing their own virtual appearance. In order to strip off the physical stigma, I will further refer to virtual robots which operate in virtual worlds as bots.

The Uncanny Valley is a phenomenon which describes the feelings of eeriness and revulsion towards a system which looks almost, but not exactly, like a real human being.

Physical robots are the most prominent category which have been associated with this issue. Obviously, roboticists have long tried to bypass the Uncanny Valley in order to perfectly mimic human beings, but their results as of now are inconclusive.

Example of Uncanny Valley

However, physical robots aren’t the only one experiencing the effects of Uncanny Valley. On their quest to becoming indistinguishable from humans, virtual bots can also become subject to this phenomenon. Through the posts they make, through the content they react to, and through their virtual appearance, bots often fail to completely blend in with fellow humans.

Moving on to the next piece of the puzzle, Recurrent Neural Networks are algorithms which, given a sequence of words, can predict what is the most likely word to come next, similarly to the word suggestions which appear at the top of your mobile keyboard. After a new word is predicted, it becomes part of the input sequence, and the RNN will then predict the word after it. By repeatedly doing this dozens of times, the algorithm will generate complete sentences, paragraphs, and so on. In order to train RNNs, people expose them to huge collections of text data on which they can learn this mapping between words.

Repeated word prediction

Similarly to the previous article, the last component of this system is based on Generative Adversarial Networks. GANs consist of a Generator and a Discriminator, endlessly competing with each other similarly to the way a counterfeiting criminal and a forensics scientist would do. After extensive escalation, the Generator eventually becomes extremely skilled at creating images, videos, and audio recordings which look indistinguishable from the authentic versions. The original content and the forged version are different, but they seem equally authentic.

The Threat

Bots do not pose a significant threat by themselves, but they can be used as building blocks in larger malevolent systems. They are like pawns on a chessboard, vulnerable alone but powerful in large numbers, and ready to be employed in a diverse set of strategies. They can be easily orchestrated at scale in order to act together for a larger predefined goal.

For example, in the architecture of DeepFake Ransomware (described in depth in the previous article), bots can be used to collect personal images from victims in order to generate fake incriminatory or intimate videos. Then, the same bots can engage in personal interaction with the victims, by showing them the disturbing content, and eventually collecting the ransom or distributing the videos. As another example, in the architecture of Computational Propaganda (described in more depth in the upcoming article), bots can be used to efficiently propagate synthetic content and increase its credibility through herd immunity.

Photo by Randy Fath on Unsplash

The sole threat posed by bots at an individual level is the danger of becoming impossible to distinguish from human users. For better or worse, AI will help virtual robots rise from the unforgiving depths of Uncanny Valley faster than their physical counterparts.

Most social media users have profile pictures which contain their portraits. Therefore, in order to blend in, bots must also have profile pictures. Obviously, bots do not have portraits, because they do not have a real physical appearance which they can transpose onto their virtual identity. Fortunately for them, Generative Adversarial Networks can be used to synthesize realistic human faces directly into digital images, just like the ones below, essentially bypassing the need to have a real face.

Generating synthetic images

You are looking at thousands of extremely realistic images which can be used as unsuspecting profile pictures. Now, try to tell if my Medium profile picture is real or synthetic. By not using images of real humans as part of their virtual appearance, bots become immune to search by image. That is, if a bot uses the image of a real celebrity or a stock photo as its profile picture, it will soon find itself in Uncanny Valley, because the image can easily be found with a reverse image search engine like Google Images or TinEye, and when it does, its credibility will vanish.

Additionally, not only can bots receive a virtual face without having a physical one, but it can be synthesized in such a way as to easily integrate a specific gender, race, and age into its identity. These characteristics allow bots to more easily blend in with people of certain demographics. In the video below, Source A represents images with specific traits, while Source B consists of a synthetic face, similar to the ones in the previous video, which gets combined with the other images in order to extract the desired demographics.

Integrating specific traits in synthetic images

Every social media user has some form of username or handle. Therefore, in order to blend in, bots must also have similar textual identifiers. A string of random characters wouldn’t look human, but a random sample of real names and surnames which match the demographics of the profile picture would certainly do the trick.

Probably the most important differentiator between human users and bots is the novel content they generate, which, on social media, usually takes the form of text. Bots don’t have personal opinions, but many people don’t either, so that’s not a problem in terms of blending in with humans. Fortunately, Recurrent Neural Networks can be used to synthesize original sequences of words designed to seem as authentic as possible, based on previous content from human users. The project referenced below automates the collection of tweets from predefined users, the training of an RNN, and the generation of new tweets.

tweet-generator by minimaxir on GitHub

To sum up, bots can be created in such a way that they become extremely difficult to tell apart from humans, by automatically integrating specific demographics into their appearance and by implementing specific mentalities into their content, based on particular communities. Obviously, bots aren’t created to just sit around and pretend to be humans. The actions they take are dictated by their creator’s goal, but at an individual level, those are the things they have to do in order to blend in.

The Defense

The easiest way for social media platforms to differentiate human users from bots is to require them to pass a CAPTCHA (completely automated public Turing test to tell computers and humans apart) when signing up and when presenting suspicious behavior. Unfortunately, most of the systems which are designed to identify bots through visual challenges fail spectacularly at the task, but do succeed in exploiting users to label data for free.

The Invisible reCAPTCHA, which aims to differentiate humans and bots by analyzing mouse movements and keyboard interactions might do the trick for several years. If implemented passively and being run the whole time on social media platforms, this type of challenge would constantly monitor peripheral devices and sensors in order to detect bots. Based on this information, platforms could send users additional challenges or temporarily suspend accounts. Bots interacting through an API wouldn’t be susceptible to these challenges.

Illustration by Adam Avery

This can easily turn into a censorship issue, but maybe we just have to strike a balance here. Platforms could also require more personal information for signing up and logging in, like a fingerprint or face, but this would certainly encounter the same issues regarding freedom of expression, going from a human-friendly space with no undercover bots to a surveillance program designed by humans for humans. However, platforms could just ask you for an Alibi, a different user which could guarantee the fact that you are human. Obviously, the Alibi would be required to have a very good human reputation and only certify a certain number of accounts.

As a fellow human user, the best way to make sure that an account belongs to a human is to look at its content. Text Generation is still in its infancy and the funny grammar present in synthetic sentences is quite easy to identify for now, as opposed to the already extremely realistic images with faces. The majority of bots do not currently employ such high-tech measures like synthetic images, so you can still use services like Botometer in order to identify bots based on more simple features, like the number of followers.

Further Reading

Yet again, Mozilla’s IRL Podcast consistently provides an intriguing piece on the subject.

Try distinguishing original poems from synthetic ones.

For more details about Obliteration as a Service, including the complete list of articles, visit this link.

TL;DR

Bots can become indistinguishable from human users through hyper-realistic virtual appearances and original content, all this with custom demographics integrated by their creators.