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Deep Fake technology

Research

Apr 26th, 2021
Is Seeing Really Believing?

Deep Fake Technologies Create New Reality

  • Research
  • Industrial Engineering

Technology has proven time and again to have had such a beneficial effect on our world, whether that be creating food sources, clean water, or even curing the coronavirus. However, the Deep Fake technology, like many other innovative tools, if in the wrong hands, could pose a tremendous threat to the world.

 

Deep Fake is an Artificial Intelligence technology that can place one`s face in the video of another person (easy replica in videos, for example). Not only does it look real, it’s also harder to detect fraud, which can easily be used for identity, financial or social fraudulence.

 

Prof. Irad Ben-Gal from the Department of Industrial Engineering, the head of The Laboratory for AI, Machine Learning, Business & Data Analytics (LAMBDA) was recently interviewed by Walla News on the risks of Deep Fake technologies during the elections. Prof. Ben-Gal explains that application of Deep Fake technologies can be legitimate and have a positive effect if properly used. Among other things, he discussed deep network models and the options nowadays to create accurate simulations of reality in applications such as smart cities and epidemiological models of infection.

 

The study

Conducted in collaboration with Dr. Erez Shmueli, from the Department of Industrial Engineering, this study showed the most effective ways of disseminating information was by using regular people in your social network who respond and discuss new opinions that you have just been exposed to. Opposite to celebrities, when regular users retell and circulate the new information, it creates local effect that makes the information more apparent on the web. This was the tactic that Cambridge Analytica used to target Facebook users during the first campaign of Trump.

This study was done as a follow up to the research of Dr. Alon Sela, who holds PhD from the Industrial Engineering Department at Tel Aviv University in collaboration with Prof. Shlomo Havlin and Dr. Louis Shechtman. They examined models of spreading opinions in modern society, and in their research, they compared two spreading mechanisms: “Word-of-Mouth” (WOM) and online search engines (WEB). In their study, the researchers applied modelling and real experimental results. This way, they compared opinions that people adopt through their friends and opinions they embrace when using a search engine based on the PageRank (and alike) algorithm.

The research findings showed that the opinions adopted through the use of WEB scheme is more homogenous. People using the search engines follow just a few dominant views without having a variety of different opinions, a phenomenon known as “the richer gets it all”. In contrast, WOM scheme produces more diverse opinions and their distribution.

 

Combining the physical world with the digital world

​The “Digital Living 2030” project, spear-headed by Ben Gal and Professor Bambos at Stanford University, is concerned with connecting the digital world and the physical worlds as trends are analyzed about our future lives 10-15 years from now. Ben Gal explains that an avatar (digital character) can be created to represent a person in the digital space for a positive purpose. “Imagine an expert radiologist whose digital character has learned from all his past data about medical imaging so that others can work using his digital character when his physical character is no longer present, for example, when he sleeps at night.”

The research helps better understand the situation with the Deep Fake technology. Since the technology is available now for commercial use, it can be “used and abused” by anyone exposed to it. Fake videos can be produced in big amounts by ordinary people that are connected on social network and can affect each other`s opinion.

In the last days of an election campaign, this kind of activity can dramatically change the results, and “even if the fraud is detected at the end of the day, it will be too late.” – says Prof. Ben-Gal. “From the other side, having a diversity of opinions, new and not ordinary, is essential and enriching.  If the application of technology is legitimate and reliable, it can enrich the body of human knowledge with diverse opinions.”

 

Loving the problem is the greatest way to invent

Research

Jul 16th, 2015
New Cyber Security study aims to identify malicious websites through their

Web Mining utilizes data mining techniques to discover and automatically learn information on the Internet (www). One of the most challenging applications of Web Mining is identifying the type of a specific website based on the category to which it belongs, and in particular identifying malicious websites. Malicious websites are websites created with the intent of harming users, stealing information or conducting other undesirable activities when the user enters the website, and afterwards.

 

In recent years, many methods have been developed for identifying website categories. Some are based on analyzing the website's textual content, its users' navigating profiles??, suspect traits found in the website itself, IP forgeries and many other properties. At the same time, the already enormous amount of information existing on the Internet continues to rise rapidly and exponentially, making the identification task ever more complex – often requiring a great deal of expensive resources.

 

A study carried out by Doron Cohen, under the supervision of Prof. Irad Ben-Gal and Prof. Shulamith Kreitler, tested a new method for identifying websites, based on an analysis of their graphic design, via methods of data mining.  To this end, an algorithm was constructed, which receives a URL as input, and produces an output by retrieving and processing all graphic design features and storing them as tables in the server. The researchers examined hundreds of home pages of websites taken from Google's top 1000 sites. For each of these, over 1000 graphic design features were examined, such as: size of area covered by each color, font size, number of characters, standard deviations, quantity and type of elements etc.

 

After processing and analyzing the data, the researchers built a predictive model based on a decision tree, and then cross-validated the model. In the first experiment they discovered that classification based solely on graphic design enables relatively high prediction of all five examined website categories (including that of malicious websites). Another experiment found that adding graphic design features to another objective prediction method can improve the precision of identification – specifically of malicious websites - by 95%, and in a statistically significant manner, using low-cost resources and low runtimes. A possible explanation for these findings is that malicious websites apparently try to conceal keywords to avoid detection by search engines, while a search based on so many graphic features can identify repeated patterns which are more difficult to conceal.

 

The study revealed that graphic design, especially colors, plays an important part in the prediction of website categories. Adding graphic properties to other predictive systems can improve their accuracy, and is therefore highly recommended. The study will be presented at the national cyber conference scheduled to take place in September at Tel Aviv University.

News - Depernent of Industrial Engineering

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