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While deepfake scams are increasing globally, new AI tools and legal frameworks help detect them and avoid possible financial or reputational loss.
Experts recommend a layered defence strategy that includes verifying the content, limiting exposure, and using multi-factor authentication.
Indian tech firms and international bodies are launching advanced tools to help spot fake media before it causes irreversible damage.
Deepfake technology has quickly moved from experimental labs to everyday digital spaces, creating new challenges for online safety. The ability to fabricate convincing audio and video now affects elections, financial security, and public trust. As these synthetic media tools advance, the need for reliable detection methods becomes more urgent.
The current situation is especially critical, since criminals are using lifelike impersonations to deceive individuals and organizations. This increase in fake content has forced cybersecurity teams, governments, and tech companies to speed up the availability of countermeasures.
Also Read: Best AI Deepfake & Scam Detection Tools to Stay Safe in 2025
Deepfake technology has advanced to the point where it can be used to produce realistic voice and face impersonations to aid phishing and fraud. The International Telecommunication Union (ITU) of the United Nations has warned that AI-manipulated videos and audios could easily compromise elections, promote misinformation, or even facilitate crimes.
Police officers and cybercrime units of India are advising the public to adopt stronger digital hygiene habits as the first line of defense against attacks based on deepfake technology. This includes creating complex passwords and activating two-factor authentication.
Spotting deepfakes is difficult, but certain cues can help:
1. Watch for unnatural lip-sync, delayed responses, or odd eye blinks.
2. Pay attention to lighting inconsistencies, skin texture, or flaky backgrounds.
3. In live calls, consider verifying identity through a secondary channel, call callback on a number you trust, or ask unexpected but verifiable questions.
Cyabra, a disinformation AI platform, introduced a deepfake detector based on two proprietary AI models, PixelProof for images (finding pixel-level anomalies) and MotionProof for videos (detecting unnatural movement), among others.
Vastav AI, developed by Indian firm Zero Defend Security, provides cloud-based detection for video, audio, and images.
Additionally, Reality Defender, XceptionNet, and Amber Video are some of the most commonly used tools that cybersecurity experts suggest for layered verification.
Research labs have successfully developed the “TruthLens” framework, which can detect deepfakes and explain the regions of the face (eyes, nose, and mouth) that were manipulated.
Academic and industry research is pushing forward in real time:
A GAN-based model developed in 2025 detected fraudulent face-manipulated media in financial transaction images with over 95% accuracy.
Zero-shot detection methods are also gaining ground; these systems can spot deepfakes using techniques like model fingerprinting, watermarking, and real-time AI monitoring.
Recent projects even employ gaze tracking during video calls. This means that by tracking where a person is looking, deepfakes that fail to mimic natural eye-contact behavior can be flagged easily.
Governments and various organizations are taking action against cybercrimes that use deepfake technology. Alecto AI is one of the companies that offer solutions for detecting altered images and submitting removal requests on various platforms for the victims of deepfake abuse.
The TAKE IT DOWN Act, signed into law in the United States in May 2025, requires the removal of non-consensual intimate deepfake content from all platforms, empowering the victims of deepfake abuse.
At the global level, the ITU has suggested the establishment of standards for content authenticity and multimedia authentication.
Also Read: AI-Powered Deepfake Detection: Challenges, Limitations, and Future Directions
Modern detection tools are vastly more powerful than earlier solutions. Instead of just binary labels (“real” or “fake”), systems like TruthLens provide explainable reasoning, making results easier to trust and act on. AI research is adopting a multi-layered defence that combines metadata analysis, behaviour analytics, and human verification, which reduces false positives and provides redundancy
The improvements in accuracy, explainability, and verification layers make modern systems far more dependable than earlier versions. With growing awareness and better technology, individuals and organizations now have a clearer path to staying protected in an increasingly digital world.
1. Can deepfakes be detected?
Yes, deepfakes can be detected, but it is becoming increasingly difficult as the technology improves. Detection methods include analyzing for visual or audio inconsistencies and using technical analysis to find metadata manipulation or software-induced artifacts.
2. What software detects deepfakes?
Reality Defender API and SDKs enable developers to use award-winning AI models to detect deepfakes and AI-manipulated media. Get started for free with 50 audio or image scans per month.
3. How many people are fooled by deepfakes?
For deepfake images, human accuracy is 62% in controlled studies. A University of Florida study found participants claimed a 73% accuracy rate in identifying audio deepfakes but were frequently fooled.
4. Can AI create a fake person?
Just provide a text description of the face you want to create, including gender, age, and ethnicity, and apps like Fotor will instantly generate a face portrait of a person that does not exist in the real world for you.
5. Which algorithm is used in deepfake detection?
Machine learning (ML) plays a pivotal role in deepfake detection. ML algorithms are trained on a vast dataset comprising both authentic and deepfake media.
