What is Deepfake? How to Detect?

Deepfakes are videos, images, or audio that are AI-generated or AI-altered. Deepfakes aim to mimic real people convincingly, doing so without their consent. Deepfakes can be used for acts like scams, political manipulation, impersonation, and reputational harm. In this blog post, we’ll be talking about how to spot a deepfake, what tools can help detect them, and what to do if you detect one.

What Is a Deepfake?

A deepfake is a kind of AI-generated media, usually created to make a person appear to say or do something they actually never did. Since this technology can replicare faces, as well as voices and expressions, these realistic looking contents can be used for fraud, blackmail, identity theft, and misinformation.

How Are Deepfakes Created?

Deepfakes are created using advanced machine learning techniques like Generative Adversarial Networks (GANs) to train models, with datasets of faces, voices, and expressions being used. These models then learn patterns like facial movements, as well as tone and speech rhythm to then generate similar content that mimics them. With the technology and training progressing, more realistic results are being put out each day.

Are Deepfakes Dangerous?

Deepfakes are dangerous since they can be used to commit fraud, impersonate other people, spread political disinformation, manipulate financial markets, blackmail victims, and damage people’s reputations. For example, audio deepfakes prevail over video ones in many sectors, including Financial Services (51%), Aviation (52%), and Crypto (55%). At the same time, Law Enforcement (56%), Technology (57%) and FinTech (57%) are reporting more face video scams. 

Large-scale deception and social engineering attacks are increasing day-by-day since the technology for creating deepfakes are becoming more realistic and accessible by the masses. According to Pindrop’s 2025 report, deepfake-fraud in contact centers surged 1,300% and projected deepfake-related fraud exposure could reach US$44.5 billion. 

Where Are Deepfakes Commonly Found?

Since the potential of things going viral and spreading quickly is bigger on social media platforms, deepfakes are mostly shared through these apps. Spreading misinformation and impersonation scams are possible through these apps, giving a clue as to why these are the most frequent methods criminals use. Messaging apps are also used, where private sharing is possible, increasing trust manipulation. 

Scam calls and AI-generated voice messages are used for fraud and identity threat, making the person believe they’re talking to someone they know or a reputable figure that they trust. Also, fake news videos published on websites are being used to influence the public’s opinion as well as political narratives.

In May 2024, UK engineering group Arup lost $25 million after fraudsters used a digitally cloned version of a senior manager to order financial transfers during a video conference. 

How Can You Detect a Deepfake? (10 Quick Checks)

1. Unnatural eye blinking 

Since training datasets often include images with eyes open, creating natural blinking patterns in deepfake videos is difficult. For those looking to spot anomalies, unnaturaly long periods without blinking, rapid micro-blinks, and eye movements that don’t follow objects or the environment can be used as clues.

2. Lip-sync mismatch 

Another one of the most common giveaways is the lip movements. If lips are out of sync with the audio, the jaw motion is stiff or limited, mouth openings are delayed, and pronunciation shapes are inconsistent with the words being spoken, the content might be a deepfake.

3. Facial expressions 

These models sometimes struggle to recreate complex or subtle emotional changes in the face. Frozen smiles, overly smooth skin, exaggerated expressions, and reactions that don’t match to the tone of the voice can clue people in. Facial shadows and muscle movement may also look weird, indicating it is a fabricated video.

4. Glitches in hair 

Hair and the fine textures of it are difficult to copy. Deepfakes may show flickering hair strands, blurred edges, strange outlines, as well as areas where the hair disappears or blends into the background.

5. Voice sounds robotic 

Audio deepfakes can be detected by paying attention to the metallic and overly polished tone. To spot an audio deepfake, paying attention to the monotonic delivery, unnatural pauses, incorrect breathing sounds, or inconsistent inflections of emotion can be clues.

6. Metadata is missing 

Metadata is there to give important clues about a file’s authenticity, including details like timestamps, device type, and editing history. Deepfake creators often alter or strip this information completely to hide manipulation traces. Checking these details may be helpful to discover a deepfake.

7. No credible source 

If the content is only on places like anonymous accounts and suspicious websites, its credibility should be questioned. Checking if a credible source like a news outlet, official channel, or a verified person posted the content may be helpful.

8. Inconsistencies between video frames

Deepfakes sometimes have details like altered facial geometry or inconsistencies in lighting between frames. Readers should watch for shifts in head shape, inconsistent shadows, changing ear or earring positions, and frame-by-frame distortions that make the video look suspicious.

9. Fails deepfake detection tools

Tools like Microsoft Video Authenticator, InVID, Deepware Scanner, or Hive AI can be used to analyse videos to discover pixel-level changes and blending errors. If the content fails these tools, it is most likely a deepfake.

10. Experts raise alerts

If reputable sources like journalists, fact-checkers, and digital forensics teams say that the content is a deepfake, it most likely is. Organizations like Reuters Fact Check, AFP, or independent OSINT experts may raise concerns about certain media, signalling that they are problematic and fabricated.

Which Tools Can Help Detect Deepfakes?

There are several tools that can help detect a deepfake by analysing pixel distortions, audio inconsistencies, and AI-generated artifacts. Microsoft Video Authenticator looks for subtle facial manipulations and gives a confidence score accordingly. Deepware Scanner scans videos and audio to look for signs of tampering, and can be used quickly for social media verification. 

Hive AI offers customers enterprise-level deepfake detection APIs that can flag both synthetic faces and voices. Another example, InVID is used by journalists to verify suspicious videos using frame-by-frame analysis and reverse image search. Lastly, Amber Video offers forensic-level detection since it examines compression patterns, facial warping, and other manipulation traces.

What Should You Do If You Suspect a Deepfake?

Once you suspect that the content you’ve come across might be a deepfake, the first step is to prevent it from being shared or forwarded further. Instead of helping spread misinformation, report the deepfake you’ve encountered to the platform, so that moderators can review your request and remove accordingly. 

Using verification tools like deepfake detectors and media forensics platforms can also help verify the video or audio. If the content might have legal, workplace, or reputational implications, saving and documenting the evidence is the right way to go, in case it is needed for legal or HR review.

Can Deepfakes Be Detected Automatically?

Deepfakes can be detected automatically with AI-powered detection tools, which analyse patterns like pixel-level inconsistencies and irregular blinking. Since these systems use machine learning models that are trained using large datasets of real and synthetic media, they can identify subtle anomalies that can be missed with the human eye. These tools should be regularly updated to make sure they’re not falling behind the ever-changing deepfake technology. 

Why Does Deepfake Detection Matter?

Deepfake detection should be taken seriously since it can protect trust and integrity in several categories. In media, the spread of misinformation and fake news is prevented thanks to detection. When it comes to elections, the democratic processes are protected thanks to detection, and manipulation and disinformation are reduced. Fraud and identity theft are prevented in finance with the usage of deepfake detection tools. It protects personal identity, privacy, and reputation of people from deepfakes created to destroy each one.