Advanced technology is changing our lives faster than most of us could have ever imagined. For example, in the 90s, no one would have believed that licensed iGaming sites like Verde Casino would be a go-to choice for gamblers rather than land-based gaming. And yet, here we are now!
Outside the Internet, the technologies that are currently all the rage are synthetic media and deepfake technology. As the two continue to rise rapidly, the world is experiencing increased business opportunities and improved audio-visual synthetic content.
However, using these technologies has also ushered in ethical and legal concerns, especially regarding the risk of trustworthiness concerns due to reports of deception and misinformation. Thus, understanding the legal and ethical issues associated with them will help develop innovative tools to detect or tackle these challenges.
What Are Synthetic Media and Deepfakes?
Synthetic media, also known as SM, describes various forms of content. It broadly involves generating creative or practical, non-deceptive content. It can be fully or partially generated AI or ML video, image, voice, or text. The following are examples of SM:
- AI written music
- OpenAI’s text generated by ChatGPT
- Voice synthesis
- Computer-generated imagery (CGI)
- Virtual reality (VR)
- Augmented reality (AR)
On the other hand, deepfakes, a subsection of SM, focus on intentionally altering audio-visual information to create fake content that looks real. Creating malicious content using deepfake tech is increasingly becoming hard to point out, which introduces a new set of ethical challenges. For example, a content creator can use advanced software to replace or manipulate the voice or face of a real person in a video with another and make it so natural that no one can tell it’s fake.
Generally, deep fakes describe fabricated content specifically altered or manipulated to achieve something. Meanwhile, SM involves using advanced technology like AI or machine learning to improve content. In this regard, deep fakes have posed various challenges in real-life situations and businesses, while synthetic media are seen as pretty beneficial, especially business-wise.
Benefits of Using SM
Synthetic media have contributed positively in various sectors, for instance:
- In marketing, it is used in product visualization. Here, advertisers employ VR to help patrons visualize how products like furniture would look in their homes
- The benefits of SM in education include learners being able to practice schoolwork through AI-generated exercises
- Medical practitioners can rehearse complicated healthcare procedures and surgeries on virtual patients and perfect their work before doing it on real people
These benefits have made SM quite popular today. Unfortunately, the rapid growth of AI, VR, AR, and ML technology has also resulted in major deepfakes that have posed more risks to human life and businesses.
Challenges of Deepfakes
Deepfake risks can involve seeing a real person’s face in a fake video or image or altering information with advanced software to make it as believable as possible. Today, the main reasons for using deep fakes include:
- Impersonating someone for personal reasons
- Fraudulent activities include changing the voice to deceive someone to give their personal information through a phone
- Spreading incorrect or false information
For example, deepfakes have been used to create realistic videos of well-known people, such as celebrities and politicians. As a result, it ends up discrediting their public work or defrauding big companies. Considering the growing number of published deepfake online videos and manipulated images, it’s becoming highly crucial to find innovative ways of detecting deepfakes and dealing with their negative impacts. They are a massive threat to identity security, privacy concerns, reputation, trustworthiness in media, and business challenges.
How to Tackle and Reduce the Negative Impacts of Deepfakes
Investing in advanced AI detection tools is among the best ways to combat the issue of deepfakes. Combining this with employee training and authentication mechanisms, companies can maintain their reputation by proactively detecting, preventing, and responding to deepfake threats. Some of the main tools used to detect deepfakes and SM include:
- A phoneme-to-viseme approach where some words are detected to be mismatched with the pronunciation
- Biological signals like natural skin color change
- Recurrent convolutional that uses deep learning models to identify inconsistency between video frames often found in deepfakes
Additionally, there should be intentional public awareness. Individuals and businesses must be educated about deepfakes and SM and how to identify and deal with their implications.
What Does the Future Hold?
For sure, it’s vital to find a balance between harnessing the benefits of AI and creating a safe online space. So, moving forward, developers, businesses and the government must come up with innovative ways to counter the misuse of AI technology that often results in deep-fake risks.