An interview with Associate Professor Duc-Tien Dang-Nguyen

Describe your journey into research from your youth up to the present. What foundational lessons did you learn from this journey? Why were you initially attracted to multimedia?

Looking back at the early days of my life, my love for science started quite young. I loved solving puzzles and small recreational mathematical problems. Actually, I still do this. It may also be because my mother “seeded” many stories with great scientific people like Thomas Edison or Marie Curie every night. I admired them a lot and often dreamed of being like them. I also love to play video games. I played them a lot, and I think that I am quite good, especially in games like The Legend of Zelda and the Castlevania series. I also love to travel, and perhaps that is why I have a nomad’s journey over the last ten years, starting from Vietnam to Japan, Italy, Ireland, and now Norway. While living in Vietnam, I would often travel to the countryside on my motorbike. Solving puzzles, playing video games, collecting things, and travelling; these tiny things play an essential role in making me who I am today.

Now back to the story. I come from Vietnam, where it is very normal for my generation to grow through endless competitions. My first challenge was a math competition when I was eight. I then became a math student and followed many competitions like the current MIT Mystery Hunt. When I was 12, a friend of my father gave me his old PC as a present. It was a 486 (we called it that since it has an Intel 486 core), and it changed my life. I played with it endlessly. I learned Pascal by myself, and in the last year of my secondary schools (K-9), I proudly won the first rank at both Math and Informatics in the regional contests. Thanks to that, I entered one of the best high schools in Vietnam. I joined the Informatics class, and as you might already guess, we were dealing with programming challenges every day. We learned mainly algorithms and data structures, discrete mathematics, and computational complexity through solving challenging problems from the International Olympiad of Informatics. It is quite similar to Topcoder now. It was tough and very competitive, but it was exciting to me since it was like solving hard puzzles.

Moving to my bachelor’s, I took an honor program in Computer Science, which was one of the best Computer Science programs in Vietnam. In the third year of my bachelor’s in an Image Processing course, I did a project about image annotation. It was a pure K-means for image segmentation based on pixel color values, followed by a k-NN on a pre-trained set of images. It sounds pretty basic now, but this was in 2001, and “I did it my way” so it was a fantastic achievement! It was from this project I became a multimedia researcher.

After my bachelor’s, I continued researching computer vision and image retrieval in my master’s. In my first year as a Ph.D. student, I was working on a multimedia retrieval project, but just three months before the qualifying exam (you need to present your research proposal to continue your Ph.D.), I changed my research topic to Image Forensics, thanks to the course of the same name. I found everything I love in this new research field. It is like solving a puzzle, collecting evidence, and playing a game simultaneously. So, I became an image forensics researcher.

Some people say, “Choose a job you love, and you will never have to work a day in your life”, perhaps they are missing the last part “because no one will hire you”. Yes, it’s just a joke, but it can also be quite true in may circumstances. It was hard to find a job that needs image forensics when I finished my Ph.D. However, since I know image processing, computer vision, and machine learning, it was not that hard for me to find a postdoc in those fields. I was then doing both multimedia forensics and multimedia retrieval. This “evolvement” introduced me to a new field, lifelogging, a research direction that tries to discover insights from personal data archives. At first, it was an “okay” field to me, but later, after digging more into it, I found many interesting challenges that need to be solved. And that was a very long story about how I reach the starting point of my research.

Can you profile your current research, its challenges, opportunities, and implications? Tell us more about your vision and objectives behind your current roles.

Bergen, where I mainly focus on image forensics and lifelogging. Multimedia forensics is about discovering the history of modifications to multimedia content such as videos, images, audio, etc. Mainly, I work with images and have dabbled a bit in video forensics. Audio is nice too, but I mostly enjoy working with the visual side of multimedia. People tend to think about multimedia forensics as a tool to check if an image or a video is real or fake. However, we also try to look at the specifics for the media in question. Some potential questions for an image could, for example, be where was it first posted? What type of camera was it taken with? These are questions that help identify the reliability of the image in questions and give more information than fake or real. Also, I believe that we should also take a further step by considering the context of use (how, where, and when) of the multimedia content. The expectation of truthfulness is radically different if the image is hanging in an art gallery than if it is being used as evidence in a court case.

As previously mentioned, I also work with lifelogging. This work is still in its early stages. We have not proposed any novel approaches yet. Instead, we are building a community by organizing research activities as workshops and bench-marking initiatives. We believe that by holding such events, we are preparing a solid user-base for the next phase when people are more familiar with such technologies, the phase of personal data analytics. We have witnessed great applications of AI during the last decade. Since AI needs data, and people need more personalized solutions, I believe that very soon we will be doing lifelogging in our everyday life. Let’s wait and see if my prediction is becoming true or not.

How would you describe your top innovative achievements in terms of the problems you were trying to solve, your solutions, and the impact it has today and into the future?

In multimedia forensics, I am quite happy that I was among the first to propose an approach for discriminating between computer graphics and natural human faces. People are well aware of “Deepfake”, and many great people are working on this problem. However, when I presented my first study in 2011, many people, including computer graphics researchers, were laughing when I told them that they would soon not be able to distinguish computer-generated faces from the real ones. In image forensics, we try to reveal all traces of the image acquisition history, and since digital images are based on pixels, they are susceptible to changes. For example, many traces of modifications become incredibly hard to find if the image is resized. Most of my approaches are thus physical or geometrical based, which makes them more robust against changes as well as more reliable in terms of decision explanation.

Over your distinguished career, what are your top lessons you want to share with the audience?

I believe that I am still at the start of my career, and perhaps the first and the most important lesson I have is about “causes and effects” or what Steve Jobs described as “Connecting the dots”. There are dots in our life that are very hard to understand or predict how everything is connected, but eventually, when looking back, the connections will reveal themselves over time. Just follow whatever you think is good for you and try very hard to make it a good “dot”. Everyone wants to work with something we love, but finding what we love in our current work is even more important.

What is the best joke you know?

Most of the jokes I love are in Vietnamese, and unless you are Vietnamese, you can’t get them. I am trying to think about some “Western” jokes that share some commonalities with Vietnamese humor and culture. That should be a politics joke. I believe that you can find a similar version with KGB or Stasi. This one was very famous, and surprisingly, it is very well suited to my current research on lifelogging 🙂

“Why do Stasi officers make such good taxi drivers? — You get in the car and they already know your name and where you live.”

Bio: Duc-Tien Dang-Nguyen is an associate professor at the University of Bergen. His main research interests are multimedia forensics, lifelogging, and machine learning.