Deepfake Detection: How To Spot Fakes In 2025 Explained

As Artificial Intelligence (AI) continues its rapid evolution and integration into our daily lives, we are finding people using it for less than savoury purposes. AI has made it even easier to create fake videos, images, and audio. The technology has reached a staggering level of sophistication, and unlike the media it makes, the threat is real. In this guide, we discuss deepfake technology, how deepfake detection works, and how you can spot them. We’ll also cover new laws and how your Web Hosting can help keep your content safe.

KEY TAKEAWAYS

• Deepfakes are created using advanced AI and are used for a range of scams, spreading misinformation, and identity theft.
• Detection tools use advanced AI models trained to identify subtle, often imperceptible anomalies in synthetic media.
• Spotting deepfakes is becoming harder due to advanced generative AI, which improves quality and introduces new manipulation techniques.
• New deepfake laws aim to give individuals stronger rights over their likeness and identity, helping to curb the misuse of deepfakes.
• Web Hosting security from Hosted.com® via SSL, firewalls, and malware scanning forms a barrier against deepfake-enabled cyberattacks.

What is a Deepfake?

At its core, a deepfake is a type of synthetic media in which someone in an existing photo or real video is replaced with another person’s face, body, voice, or entire identity. This isn’t your average Photoshopping; deepfakes are created using state-of-the-art machine learning models and deep learning techniques, particularly Generative Adversarial Networks (GANs) or Convolutional Neural Networks (CNNs). This makes them incredibly realistic and hard to spot. They come in a few flavors, including:

Deepfake Video Makers & Photos

These are probably the most well-known examples. They involve manipulating photos and footage so it looks as if someone is saying or doing something they never said or did. This could range from face swaps with an AI-generated image for identity fraud to creating fake intimate videos for blackmail.

Audio (Voice Cloning)

Not just limited to fake images and videos, audio deepfakes can almost perfectly imitate a person’s voice, allowing them to generate speech that sounds exactly like the real person is speaking. Imagine receiving a phone call from what sounds like your bank, only to discover it’s an AI-generated voice clone you’ve just given your account number to.

According to Matthew Wright, PhD, a professor and Chair of Cybersecurity at Rochester Institute of Technology, “these crimes are happening more because the technology to create such voices is getting better,” in an article on  January 29, 2024. (Source)

Synthetic Identity Manipulation

This involves creating entirely new non-existent identities from scratch or the sophisticated manipulation of real ones through the use of AI. A good example of this is the use of AI models to create realistic fake profiles on social media platforms, complete with visual content and backstories, making it nearly impossible to check if they are real or not.

The Deepfake Threat Landscape

From people doing things they never did to cloned voices authorizing bank transactions, the ways deepfakes are being used for harm are growing. They have the potential not only to affect businesses but also various aspects of our personal lives. The risks are not theoretical; they are already happening.

The ability to convincingly impersonate real people opens up an almost endless list of ways for deepfake AI to do damage. Cybercrime involving deepfake technology has increased by over 700% in the past year.

A good example of video manipulation is the case of a financial worker at a large multinational firm who was tricked into paying $25.6 million to scammers they believed were staff members after receiving a phishing message from an email address that appeared to belong to the CFO.

According to senior superintendent Baron Chan Shun-ching, talking to Radio Television Hong Kong (Source) on February 2, 2024, “(In the) multi-person video conference, it turns out that everyone [he saw] was fake”.

Beyond financial fraud schemes, deepfakes can be used for advanced identity theft. By creating believable fake media or imitating real people, they can gain access to sensitive information, bank accounts, or even commit crimes, appearing to be someone else.

On a more personal level, deepfake videos of people making controversial statements or performing illegal or explicit activities for blackmail, revenge, or public shaming can quickly go viral. This is probably the most sinister and disturbing threat, not only to public figures but to pretty much anyone on a social network, especially thanks to social search.

To further highlight the rising threat, a survey conducted by The Alan Turing Institute found that over 90% of respondents are worried about the spread of deepfakes.

How Deepfake Detection Works

As deepfakes become more sophisticated, so do the ways to find them. Deepfake detection technology is based on identifying the often imperceptible, unintended inconsistencies (known as artifacts) that are tell-tale signs of generative AI manipulation and synthetic media. Below are some of the ways used to detect fakes.

Micro-Expressions

Real human faces have subtle, involuntary movements, blinking patterns, and muscle contractions called micro-expressions. Video deepfake generation techniques often struggle to replicate these physiological cues accurately. Detection systems can analyze blink rates, eye movements, pupil dilation, and inconsistencies in facial expressions that are difficult for synthetic media to mimic in real time.

Asymmetry & Distortion

While fake images aim for realism, minor asymmetries or distortions in facial features, especially around the edges where one face is swapped onto another, can sometimes be detected by deepfake detection algorithms. This could be unnatural blending lines, distorted teeth, or hair that doesn’t quite look natural.

Lighting & Shadows

Recreating realistic lighting and shadows when manipulating an existing video or image can be incredibly difficult for AI. Deepfake image detection models can detect inconsistencies in lighting, shadows in incorrect places, or wrong tones that indicate tampering.

Timing Inconsistencies

Real videos are fluid and consistent, while lower-quality deepfakes may show jerky movements, jumps, or unnatural transitions due to AI continuity issues. Similarly, the audio and video often don’t line up perfectly. This can appear as lip movements that don’t quite match the speech or a slight lag between what you see and hear.

Artifact & Forensic Analysis

Deepfake images and videos often leave behind digital fingerprints or subtle artifacts due to the compression and rendering processes. Advanced detection tools can analyze these artifacts to detect suspicious data.

Beyond AI models, traditional digital forensic analysis also plays a role. Media files often contain metadata, like creation dates, the software used, and device information. Deepfakes may have missing or altered metadata, indicating tampering.

Forensic experts can examine pixels and image metadata for inconsistencies, compression errors, or patterns that indicate AI-generated content rather than a legitimate image.

How to Detect Deepfakes Manually: Clues & Red Flags

Even with advanced detection capabilities available, the human eye can be a reliable way to spot a deepfake compared to the real thing. Manual spotting works best when carefully observing facial motion, lighting, expression, and context. Here’s what to look for:

• Unnatural blinking, fixed gaze, robotic facial movements.
• Asymmetrical facial features or lighting mismatches.
• Lack of fine details like pores, scars, and wrinkles.
• Words not precisely matching mouth movements.
• Abrupt or off-tone voice transitions.
• Flickering or warping in the background.
• Hair blending into the background or sudden appearance shifts.
• Inconsistent lighting on faces vs background.
• Missing or incorrect shadows under the chin or nose.
• Lack of authentic emotional expression, eyes not tracking the camera naturally.
• Overly neutral or flat delivery and robotic tone, inconsistent cadence, and abrupt voice breaks.

Challenges in Deepfake Detection: Why It’s Getting Harder

While strides have been made to identify fake digital media, the rate at which it is advancing means that deepfake detection systems are essentially playing catch-up. This essentially means that what worked yesterday might be ancient history tomorrow, making it an uphill battle.

With AI deepfake videos increasing by 550% between 2019 and 2024, and over 500,000 deepfakes shared on social media in 2024, it’s easy to see why it’s getting harder to spot them.

Evolving Generation Tools

Firstly, detectors often find it challenging to stay ahead of new generative AI methods used for creating deepfakes, especially considering the vast amount of fake content available online and in the media, which makes filtering through this much more difficult.

Early deepfakes often had fairly noticeable flaws, including fake faces with unnatural head poses, weird distortions, or inconsistent lighting. Detection frameworks were trained to spot these “tells”. However, the deep neural networks behind this AI threat have become extremely adept at creating highly realistic content.

AI tools like Midjourney, DALL·E-3, and Stable Diffusion can create ultra-realistic synthetic images and videos, making them increasingly easier to use and more accessible than ever. For example, face-swapping has evolved to include full-body manipulation, voice cloning, and even entire videos that are created from text prompts. Each of these requires different detection methods.

Lack of Standardized Datasets

The AI used for detection needs massive and diverse amounts of data from both real and fake media for training. The Deepfake Detection Challenge (DFDC) is designed to measure progress on deepfake detection technology. The open-source DFDC dataset is the largest, comprising 23,654 original videos and 104,500 corresponding deepfakes generated from the originals to learn from each other.

Creating datasets that cover the latest methods across different conditions, ethnicities, ages, and genders is a logistical challenge, to say the least. This can lead to models that perform well on specific types of media or demographics but fail on others.

A detector trained on older data may also struggle to identify new deepfakes created with more advanced techniques it hasn’t encountered before, reducing accuracy rates.

Bypassing Detectors

One of the most worrying recent developments is the rise of deepfake techniques specifically made to bypass AI detection systems. This involves creators deliberately adding perturbations (small changes) into the generated content.

These perturbations are created to trick detection algorithms by pushing them past their classification boundaries. While invisible to humans, these changes can confuse the learning patterns that deepfake detection tools rely on, making fakes appear real to them.

This is what is called an adversarial attack. What happens is that the AI’s accuracy to identify things correctly can drop by sometimes more than 10% to 20%.

Next, when images or videos are compressed (e.g., for sharing on social media or messaging apps), they often introduce compression artifacts. Deepfake creators can use these by making their fakes more resistant to compression, so they don’t break down and reveal their artificial nature, or by mimicking the real media’s artifacts. This can make it incredibly difficult for a detector to distinguish between normal compression noise patterns and the signs of a deepfake.

Deepfake Law & New Legislation

While many countries are beginning to enact legislation, the regulation of deepfake web content is fragmented. Some have specific laws against explicit content, while others attempt to address it through existing laws. This creates varying definitions, penalties, and safeguards for deepfakes, with varied approaches for each. Despite this, government agencies are attempting to address the issue.

Signed into law in May 2025, the TAKE IT DOWN (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks) Act aims to criminalize the non-consensual publication of intimate images, including deepfakes. It requires sites and platforms to implement a notice-and-removal process.

“The TAKE IT DOWN Act is a historic win for victims of revenge porn and deepfake image abuse,” said Senator Ted Cruz on May 15, 2025 (Source)

Many other countries are enacting or considering similar legislation. The EU AI Act, for instance, emphasizes transparency, requiring creators to disclose when their content has been generated by AI.

In some recent deepfake news, Denmark is considering passing legislation that would treat a person’s likeness as their intellectual property, essentially protecting it under copyright law. This marks the first time any country has proposed giving people copyright over their physical traits and voices.

Danish culture minister, Jakob Engel-Schmidt, speaking to The Guardian on June 26, 2025, said: “In the bill we agree and are sending an unequivocal message that everybody has the right to their own body, their own voice and their own facial features, which is not how the current law is protecting people against generative AI”. (Source)

Web Hosting Security Against Deepfake-Enabled Threats

With deepfakes enabling more realistic phishing and impersonation attacks, securing your web hosting environment is more important than ever. While deepfakes target identity, they often exploit weak infrastructure. At Hosted.com®, we take cybersecurity seriously, ensuring your site, accounts, and visitors are protected with the best available infrastructure and software.

We provide a free SSL (Secure Sockets Layer) certificate to encrypt all data transfers and prevent interception or manipulation.

You also receive Web Application Firewalls (WAFs) that block suspicious traffic, filter malicious requests, and guard against common attacks such as cross-site scripting (XSS) or SQL injection, which are often paired with deepfake-based scams.

Then there’s anti-malware and detection to scan for viruses, harmful code, and unauthorized script injections that can be triggered via fake user activity or phishing campaigns initiated by deepfakes.

Finally, we undertake daily automated backups with easy recovery in case deepfakes are used to gain unauthorized access and damage your site or steal data.

FAQS

Other Blogs of Interest

– Exploring AI Domains: The Future of Web Addresses

– Why Are .ai Domains So Expensive? The Truth Revealed

– Best AI Website Builder: Create a Site in Minutes with AI

– AI Website Builders: Sacrificing Creativity For Speed?

– Hosted.com®’s NEW AI Domain Name Generator Is Here!