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Living in the contemporary world means that information can be created, spread and shared with little effort. However, as new media platforms enter the social sphere, they also bring with them employment opportunities that deceive the public and erode their credibility. Among the technologies of most concern in this area are deepfakes: images, voices, and videos depicting fake persons and situations that seem real and can be closely associated with a person’s actual actions. Given the concern with so much deepfake content flooding the market, now is the time to build and install a deepfake detection system.
Deepfakes are created with the help of Artificial Intelligence and are based on deep learning using Generative Adversarial Networks (GAN). In other words, GAN is a competition between two algorithms: a generator that generates fakes and a discriminator that seeks to identify them. Over thousands of cycles, the generator optimizes its content generation results and it becomes harder for the discriminator to distinguish fake from real data samples.
This separation is something that governments, companies, and media organizations need to get right to restore their credibility among citizens. We can see the risks in the world of journalism. In the age of ‘fake news’ adding actual deep fakes to the mix can only reduce the credibility of the source. Suppose a news channel is broadcasting a fake video live of a political figure supporting toxic policies.
Often producing a follow-up video exposing the deepfake later can be helpful but, in many cases, the damage to the targeted politician’s reputation has already been done.
However, it is important to know that deepfakes are a threat to democracy. They are so influential that they can sway the outcome of any election by promoting fake news at unprecedented speed. In the election period, deepfakes will create misunderstanding and propaganda among voters and spread misinformation about a particular candidate. When such deepfakes go unnoticed, individuals may no longer trust a given video or audio as genuine, meaning the population will lose trust in the very media that is expected to advance democracy. She goes. This breakdown of trust can create skepticism and political apathy that destroy the democratic framework for knowledge-based decision making.
Another potential application of deepfake technology emerged as one of the worst use cases: revenge porn where victims’ faces were then superimposed onto pornographic content created with someone else. This invasion of privacy not only causes suffering to people but can cause personal psychological trauma, social exclusion and reputational damage.
To deal with the problems of deepfakes, one has to proceed with the option of an interdisciplinary approach that would involve multiple aspects of the work. To start, there needs to be investment in identity technology. Advertisers, customers and governments must contribute resources to fund ongoing research that will ensure detection tools remain as effective as possible in identifying these deceptive tactics. Social networks also have more responsibility.
Public awareness is also important. This means that increasing awareness among the general public regarding deepfakes and training independent models to detect fake content will make it easier to curb fake content distribution. This education can extend to schools so that learners can develop relevant skills when faced with challenging media environments. Also, media organizations need new means to address the issue of deepfake verification as fact-checking processes must adapt to such new phenomena. Through this, they can also increase their credibility by publicly broadcasting their fake identity programs.
We are at a stage where technology can either strengthen that trust or begin to break it down. It is time to take deepfakes seriously and protect the truth in the internet sphere to maintain its core value today. Older generations believe that trust is the foundation of society and without it, people keep spreading rumours, fear and suspicion. Protecting that trust has never been more important than it is today, and the ability to counter deepfakes is one of the most important weapons in that fight.
This article is written by Nimeshkumar Patel, Senior Network Engineer and Architect, Humana Inc.
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