Deepfakes are fake media in which a person’s likeness is used to replace their original appearance in a picture or video. Deep learning and generative adversarial networks (GANs) are two examples of artificial intelligence approaches that are commonly used to do this.

Deepfakes digitally change and replicate actual people using deep learning methods, such as generative adversarial networks. Harmful instances have included imitating a manager’s directions to workers, creating a bogus letter for a family in need, and spreading fabricated humiliating images of people.


As deepfakes become more convincing and challenging to spot, cases like this are increasing. Thanks to advancements in instruments developed for legitimate purposes, they are also simpler to produce. Microsoft, for instance, recently unveiled a brand-new language translation tool that resembles an individual’s voice in a different language. However, a major worry is that those resources also make it simpler for offenders to interfere with the business’s activities.

Threats of Deepfake technology 

Deepfakes have the following main consequences, to name a few:

  •  False information and mistrust – Videos and news articles depicting persons acting or talking in ways they never did can be created using deepfakes. This causes misinformation to propagate and damages viewers’ faith in the media. People find it more difficult to discriminate between authentic and false content.
  • Non-consensual deepfakes, such as fake pornographic clips, violate individual confidentiality and their right to image autonomy. For women who are frequently the targets, this is extremely troublesome. It results in emotional distress. 
  • Deepfakes enable the production and dissemination of manipulated media that can be used to sway voters or the public’s opinion, posing concerns for election campaigns and voting. The democratic methods are hampered by this.
  • Threats to national security: malicious parties may utilize Deepfakes to produce fake information that endangers national security or engage in identity theft, fraud, or pranks. Political disputes may be brought up by misleading media.
  • Harassment of marginalized populations – There are worries that the propagation of purposefully false and altered media may be exploited by deepfakes to identify, bully, or harm already marginalized populations.
  • Loss of confidence in technology – As people become more suspicious of what is real vs fraudulent online, extensive deepfake exploitation may cause consumers to gradually lose faith in AI technologies.
  • Identity theft is fueled by deep fakes. For businesses, identity theft might result in more serious issues. Cybercriminals can employ deepfake on counterfeit credentials to begin fabricated stories or fabricate claims that could ruin an organization’s image, in addition to causing financial losses to businesses.

Who creates deepfakes?

Everyone, including academic and business researchers, individuals, CG studios, and porn makers. Governments may be experimenting with the technology as part of their web-based strategy to undermine and delegitimize extremist groups or communicate with particular people, for example.


With more sophisticated AI picture generators, it is becoming more difficult to spot deepfakes. World governments and police organisations are concerned about the impact of AI-generated deepfakes on social networks and in places of conflict. Marko Jak, co-founder and CEO of Secta Labs, claims and predicts that in a year or less it will be hard to tell if an image is fraudulent upon first sight. “We’re getting into an era where we can no longer believe what we see,” says Marko Jak, co-founder and CEO of Secta Labs. “Right now, it’s easier because the deepfakes are not that good yet, and sometimes you can see it’s obvious.” 

Types of Frauds

According to Robert Scalise, worldwide managing partner of risk and cyber strategy at Tata Consultancy Services (TCS), deepfake attacks can be divided into four main groupings:

  • Information that is false, misleading, or harmful.
  • theft of intellectual property.
  • Defamation.
  • Pornography.

Which technology is required to create deepfakes?

It’s difficult to make a good deepfake on a standard PC. Most of them are created on cutting-edge desktop PCs with powerful graphics cards, or even more effective, cloud computing resources. As a result, processing time is reduced from days to weeks to hours. However, a number of technologies are now available to help in the production of deepfakes. Numerous companies that handle every stage of processing on the cloud are available to make them for you. Even on mobile devices, people may add their faces to a library of TV and movie stars that the system has been trained on using the Zao app.


The best ways to spot fraudulent deep learning technology 

Deepfakes detection can now be achieved by using a mix of scientific and artistic skills.

Humans may be able to tell when an AI-generated person has off-kilter voice cadences or unnatural shadows around their eyes.

When distinguishing between actual and fraud photographs, humans can seek for a number of telltale indicators, such as the following:

  • Differences in the layers of skin and other organs in the body.
  • dark circles around the eyes.
  • Blinking patterns that are unusual.
  • Unusual reflection on the glasses.
  • Lips gestures that seem unrealistic.
  • Lip coloring is unusual in comparison to the face.
  • Face and facial hair are incompatible.
  • Face with fake moles.

However, thanks to modern technology, many of these old “tells” are no longer valid. Today, red flags could show up as irregularities in coloring and highlighting, which deepfake technology is still working to improve.

The Solution 

Businesses can take a number of measures in the interim to get ready for the prevalence and severity of deepfake violations, from simple employee training to more advanced identification and safety methods and processes.

The following severe safety precautions could aid in protecting against nefarious applications of deepfake technology:

  • Digital watermarking: Methods for authenticating media files by adding undetectable watermarks to them. Watermarks can withstand minimal editing and be recognised by apps for confirmation.
  • Fingerprinting methods – Examining distinctive artefacts or fingerprints left by recording or editing equipment. A video’s manipulation status can be determined by comparing fingerprints. 
  • Anomaly Detection Models: AI systems that have been trained on big authentic datasets to spot tiny abnormalities that point to content manipulation. Additional information collected over time makes models smarter.
  • Centralised Database of Deepfakes – Using perceptual hashing techniques, it may be possible to quickly recognise newly created fakes from a database of deepfake movies that are already known to exist. 
  • Tamper-Proof Media Formats – Using blockchain and other shared database technologies, original media content might be timestamped and authenticated in a way that makes any subsequent tampering detectable.
  • Regulating  Deepfake Tools – Accessibility to deepfake generation tools should be restricted, and any publically available content should be watermarked or authenticated to prevent exploitation.
  •  Media literacy education teaches people how to think critically so they can more easily spot misleading media solely by examining statements, information, and contextual cues.

To know more about the threat of Deepfakes AI Technology and how to detect and avoid it, you can refer to the following useful YouTube videos:

Video 1:

Video 2:



The potential risk of deepfakes comes from the level of perfection at which they are manipulated, making it occasionally appear hard to tell them apart from actual films. As a result of technological advancements, it is now possible to deftly alter voices and faces to convey imaginary events. While this presents privacy and ethical issues, deepfake technology also holds promise for positive uses, such as digital history or fun. The secret involves applying it in a way that doesn’t deceive or hurt other people

Rohan Pradhan

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