The impressive but controversial DeepFakes, images and video manipulated or generated by deep neural networks, are likely to get both more impressive and more controversial in the near future, according to Hao Li, the Director of the Vision and Graphics Lab at the University of Southern California. Li is a computer vision and DeepFakes expert, and in a recent interview with CNBC he said that “perfectly real” Deepfakes are likely to arrive within half a year.
Li explained that most DeepFakes are still recognizable as fake to the real eye, and even the more convincing DeepFakes still require substantial effort on the part of the creator to make them appear realistic. However, Li is convinced that within six months, DeepFakes that appear perfectly real are likely to appear as the algorithms get more sophisticated.
Li initially thought that it would take between two to three years for extremely convincing DeepFakes to become more commonplace, making that prediction at a recent conference hosted at the Massachusetts Institute of Technology. However, Li revised his timeline after the revelation of the recent Chinese app Zao and other recent developments concerning DeepFakes technology. Li explained to CNBC that the methods needed to create realistic DeepFakes are more or less the method currently being used and that the main ingredient which will create realistic DeepFakes is more training data.
Li and his fellow researchers have been hard at work on DeepFake detection technology, anticipating the arrival of extremely convincing DeepFakes. Li and his colleagues, such as Hany Farid from the University fo California Berkely, experimented with state of the art DeepFake algorithms to understand how the technology that creates them works.
Li explained to CNBC:
“If you want to be able to detect deepfakes, you have to also see what the limits are. If you need to build A.I. frameworks that are capable of detecting things that are extremely real, those have to be trained using these types of technologies, so in some ways, it’s impossible to detect those if you don’t know how they work.”
Li and his colleagues are invested in creating tools to detect DeepFakes in acknowledgment of the potential issues and dangers that the technology poses. Li and colleagues are far from the only group of AI researchers concerned about the possible effects of DeepFakes and interested in creating countermeasures to them.
Recently, Facebook started a joint partnership with MIT, Microsoft and the University of Oxford to create the DeepFake Detection Challenge, which aims to create tools that can be used to detect when images or videos have been altered. These tools will be open source and usable by companies, media organizations, and governments. Meanwhile, researchers from the University of Southern California’s Information Sciences Institute recently created a series of algorithms that could distinguish fakes videos with around 96% accuracy.
However, Li also explained that the issue with DeepFakes is the way they can be misused, and not the technology itself. Li noted several legitimate possible uses for DeepFake technology, including in the entertainment and fashion industries.
DeepFake techniques have also been used to replicate the facial expressions of people with their faces obscured in images. Researchers used Generative Adnversail Networks to create an entirely new face that had the same expression of a subject in an original image. The techniques developed by the Norwegian University of Science and Technology could help render facial expressions during interviews with sensitive people who need privacy, such as whistleblowers. Someone else could let their face be used as a stand-in for the person who needs anonymity, but the person’s facial expressions could still be read.
As the sophistication of Deepfake technology increases, the legitimate use cases for Deepfakes will increase as well. However, the danger will also increase, and for this reason, the work on detecting DeepFakes done by Li and others grows even more important.
Startups Creating AI Tools To Detect Email Harassment
Since the Me Too movement came to prominence in late 2017, more and more attention is paid to incidents of sexual harassment, including workplace harassment and harassment through email or instant messaging.
As reported by The Guardian, AI researchers and engineers have been creating tools to detect harassment through text communications, dubbed MeTooBots. MeTooBots are being implemented by companies around the world in order to flag potentially harmful and harassing communications. One example of this is a bot created by the company Nex AI, which is currently being used by around 50 different companies. The bot utilizes an algorithm that examines company documents, chat and emails and compares it to its training data of bullying or harassing messages. Messages deemed potentially harassing or harmful can then be sent to an HR manager for review, although Nex AI has not revealed the specific terms that the bot looks for across communications it analyzes.
Other startups have also created AI-powered harassment detection tools. The AI startup Spot owns a chatbot that is capable of enabling employees to anonymously report allegations of sexual harassment. The bot will ask questions and give advice in order to collect more details and further an investigation into the incident. Spot wants to help HR teams deal with harassment issues in a sensitive manner while still ensuring anonymity is preserved.
According to The Guardian, Prof. Brian Subirana, MIT and Harvard AI professor, explained that attempts to use AI to detect harassment have their limitations. Harassment can be very subtle and hard to pick up, frequently manifesting itself only as a pattern that reveals itself when examining weeks of data. Bots also can’t, as of yet, go beyond the detection of certain trigger words and analyze the broader interpersonal or cultural dynamics that could potentially be at play. Despite the complexities of detecting harassment, Subirana does believe that bots could play a role in combating online harassment. Subirana could see the bots being used to train people to detect harassment when they see it, creating a database of potentially problematic messages. Subirana also stated that there could be a placebo effect that makes people less likely to harass their colleagues even they suspect their messages may be being scrutinized, even if they aren’t.
While Subirana does believe that bots have their potential uses in combating harassment, Subirana also argued that confidentiality of data and privacy is a major concern. Subirana states that such technology could potentially create an atmosphere of distrust and suspicion if misused. Sam Smethers, the chief executive of women’s rights NGO the Fawcett Society, also expressed concern about how the bots could be misused. Smethers stated:
“We would want to look carefully at how the technology is being developed, who is behind it, and whether the approach taken is informed by a workplace culture that is seeking to prevent harassment and promote equality, or whether it is in fact just another way to control their employees.”
Methods of using bots to detect harassment and still protect anonymity and privacy will have to be worked out between bot developers, companies, and regulators. Some possible methods of utilizing the predictive power of bots and AI while still safeguarding privacy include keeping communications anonymous. For instance, reports could be generated by the bot that only includes the presence of potentially harmful language and counts of how often the possibly harassing language appears. HR could then get an idea if uses of toxic language are dropping following awareness seminars, or conversely could determine if they should be on the lookout for increased harassment.
Despite the disagreement over appropriate uses of machine learning algorithms and bots in detecting harassment, both sides seem to agree that the ultimate decision to get intervene in cases of harassment should be by a human, and that bots should only ever alert people to matched patterns rather than saying definitively that something was an instance of harassment.
AI Security Monitoring & Job Recruitment Companies Raise Funds
VentureBeat reports on two new high fundings for startups developing artificial intelligence. Umbo Computer Vision (UCV) works on autonomous video security systems to businesses, while Xor is developing an AI chatbot platform for recruiters and job seekers. Both startups are located in San Francisco, and UCV is a joint venture with Taiwan and has a base there and in the UK too. UCV raised $8 million for its Ai-powered video security, while Xor managed to raise $8.4 million for its project.
Xor’s capital infusion came after a year in which the startup tripled its sales in the US, “reaching $2 million in annual recurring revenue and closing deals with over 100 customers in 15 countries, including ExxonMobil, Ikea, Baxter Personnel, Heineken, IBS, Aldi, Hoff, McDonald’s, and Mars.” As the company co-founder and CEO Aida Fazylova explains, she “started the company to let recruiters focus on the human touch — building relationships, interviewing candidates, and attracting the best talent to their companies. Meanwhile, AI takes care of repetitive tasks and provides 24/7 personalized service to every candidate. We are proud to get support from SignalFire and other amazing investors who help us drive our mission to make the recruitment experience better and more transparent for everyone.”
Xor’s chatbot “automates tedious job recruitment tasks, like scheduling interviews; sorting applications; and responding to questions via email, text, and messaging apps like Facebook Messenger and Skype. The eponymous Xor — which is hosted on Microsoft’s Azure — draws on over 500 sources for suitable candidates and screens those candidates autonomously, leveraging 103 different languages and algorithms trained on 17 different HR and recruitment data sets.”
According to Grand View Research, the chatbot market is expected to reach $1.23 billion by 2025, while Gartner predicts that chatbots will power 85% of all customer service interactions by the year 2020.
For its part, Umbo develops “ software, hardware, and AI smarts that can detect and identify human behaviors related to security, such as intrusion, tailgating (when an unauthorized individual follows someone into private premises), and wall-scaling.”
The company says it has developed its AI systems entirely in-house, and their system incorporates three components.“AiCameras are built in-house and feature built-in AI chips, connecting directly to the cloud to bypass servers and video recording intermediates, such as NVRs or DVRs. Light is AI-powered software for detecting and issuing alerts on human-related security actions.” There is also “TruePlatform, a centralized platform where businesses can monitor and manage all their cameras, users, and security events.” As Shawn Guan, Umbo’s cofounder and CEO points out, the company launched Umbo Light, “which implemented feedback that we gathered from our customers about what their primary wants from video security systems were. This allowed us to design and deliver a system based on the needs of those who use it most.”
The global video surveillance market, which is now practically relying on the use of AI, was pegged at $28 billion in 2017 and is expected to grow to more than $87 billion by 2025.
Cybersecurity Experts Defend from AI Cyberattacks
Not everybody with good intentions is set to use the advantages of artificial intelligence. Cybersecurity is certainly one of those fields where both those trying to defend a certain cyber system and those trying to attack it are using the most advanced technologies.
In its analysis of the subject, World Economic Forum (WEF) cites an example when in march 2019, “the CEO of a large energy firm sanctioned the urgent transfer of €220,000 to what he believed to be the account of a new Eastern European supplier after a call he believed to be with the CEO of his parent company. Within hours, the money had passed through a network of accounts in Latin America to suspected criminals who had used artificial intelligence (AI) to convincingly mimic the voice of the CEO.” For their part, Forbes cites an example when “two hospitals in Ohio and West Virginia turned patients away due to a ransomware attack that led to a system failure. The hospitals could not process any emergency patient requests. Hence, they sent incoming patients to nearby hospitals.”
This cybersecurity threat is certainly the reason why Equifax and the World Economic Forum convened the inaugural Future Series: Cybercrime 2025. Global cybersecurity experts from academia, government, law enforcement, and the private sector are set to meet in Atlanta, Georgia to review the capabilities AI can give them in the field of cybersecurity. Also, Capgemini Research Institute came up with a report that concludes that building up cybersecurity defenses with AI is imperative fro practically all organizations.
In their analysis, WEF, indicated four challenges in preventing the use of AI in cybercrime. The first is the increasing sophistication of attackers – the volume of attacks will be on the rise, and “AI-enabled technology may also enhance attackers’ abilities to preserve both their anonymity and distance from their victims in an environment where attributing and investigating crimes is already challenging.”
The second is the asymmetry in the goals – while defenders must have a 100% success rate, the attackers need to be successful only once. “While AI and automation are reducing variability and cost, improving scale and limiting errors, attackers may also use AI to tip the balance.”
The third is the fact that as “organizations continue to grow, so do the size and complexity of their technology and data estates, meaning attackers have more surfaces to explore and exploit. To stay ahead of attackers, organizations can deploy advanced technologies such as AI and automation to help create defensible ‘choke points’ rather than spreading efforts equally across the entire environment.”
The fourth would be to achieve the right balance between the possible risks and actual “operational enablement” of the defenders. WEF is the opinion that “security teams can use a risk-based approach, by establishing governance processes and materiality thresholds, informing operational leaders of their cybersecurity posture, and identifying initiatives to continuously improve it.” Through their Future Series: Cybercrime 2025 program, WEF, and its partners are seeking “to identify the effective actions needed to mitigate and overcome these risks.”
For their part, Forbes has identified four steps of direct use of AI in cybersecurity prepared by their contributor Naveen Joshi and presented in the bellow graphic:
In any case, it is certain that both defenders and attackers in the field of cybersecurity will keep on developing their use of artificial intelligence as the technology itself reaches a new stage of complexity.