Deepfake technology – once a niche experiment confined to research labs – has evolved at a staggering speed into a global threat. Tools that can convincingly swap faces, mimic videos or alter images are now widely accessible, outpacing public understanding and enterprise preparedness. The impact is already being felt, with deepfakes increasingly used in biometric fraud, exposing new vulnerabilities for organizations and consumers alike.
As synthetic media becomes indistinguishable from reality, the foundations of digital trust are eroding. Traditional trust signals – logos, familiar faces, recognized voices or live videos – are no longer reliable. This is not just a technical challenge, but a human one. Recent research shows that human detection of high-quality deepfake videos is only 24.5% accurate. In an environment where reality can be convincingly forged, seeing is no longer believing, and trust must be rebuilt.
The Erosion of Digital Trust
Deepfakes pose a profound and major threat to digital trust. At their core, they exploit human trust, leveraging our natural tendency to believe what we see and hear. As a result, deepfakes are not only deceiving people, but they also actively bypass traditional security measures that were never designed to question hyper-realistic audio or video that mimics individuals.
This capability has dramatically amplified the effectiveness of social engineering attacks. Fraudsters can now impersonate executives, colleagues or trusted public figures to authorize transactions, extract sensitive information or manipulate decision-making in real time. Beyond financial crime, deepfakes blur the line between truth and fabrication, challenging the integrity of digital communications, news and official records.
The scale of this threat is accelerating rapidly. Deepfake-driven fraud attempts surged last year, with an attack taking place every five minutes. The consequences extend far beyond immediate financial losses, creating lasting reputational damage to organizations and impacting public confidence in digital systems. Left unchecked, deepfakes risk creating an environment of uncertainty where trust is fragile, and deception is increasingly difficult to contain.
Strategies for Building Digital Trust
To stay ahead of the rapidly evolving threats, organizations must shift from reactive checks to a proactive and layered authentication strategy - one that verifies not just identity, but also media integrity, device trust, and behavioral signals in real-time.
This is where platforms like Incode’s Deepsight come into play. Deepsight is a breakthrough AI defense that detects and blocks deepfakes, injected virtual cameras, and synthetic identity attacks before damage occurs. By applying multi-modal AI, the platform analyses video, motion, device, and depth data to expose inconsistencies that synthetic media cannot reproduce, all in under 100 milliseconds. This enables enterprises to stop sophisticated fraud attempts in real time, without adding friction for users.
Deepsight’s performance has been independently validated. Its models were benchmarked in Purdue University’s study, “Fit for Purpose? Deepfake Detection in the Real World”, which evaluated 24 detection systems across commercial, government, and academic providers. Incode achieved the highest accuracy and the lowest false acceptance rate among commercial tools, outperforming both government and academic models.
The platform also anchors Incode’s broader investment in frontier AI research for identity and trust, including Agentic Identity, which securely connects verified humans to AI agents acting on their behalf. These capabilities provide an assessment of identity across three primary layers:
- Behavioural: Detects subtle interaction anomalies from AI bots or fraud farms
- Integrity: Verifies camera and device authenticity to block virtual media
- Perception: Identifies deepfakes from genuine human users through AI analysis across multiple capture modalities, such as video, motion, and depth
By unifying these layers into a single, real-time defense, Deepsight enables enterprises to restore confidence in digital systems, shifting trust to what can be continuously proven.
Business Best Practices
Beyond the technical solutions, rebuilding digital trust requires organizational commitment. Strengthening employee awareness and training is vital, as simulated deepfake attacks and scenario-based exercises can help employees recognize, question and report suspicious activity to become an active line of defence.
Additionally, businesses should have clear policies and guidelines for the ethical use of AI, content validation and incident response protocols. This includes frameworks for identifying, escalating and containing deepfake-related incidents, as well as plans to manage reputational and operational impact. Crucially, rebuilding trust requires collaboration across security, identity management and fraud prevention teams to ensure a cohesive strategy that aligns technology, people and process is in place.
Looking to the Future
Ultimately, long-term digital trust will depend on three forces working together: smart regulation that keeps pace with innovation, AI-powered safeguards like Deepsight that can detect and stop threats in real time, and transparent communication that empowers users to understand what is real - and what is not.
Deepsight reflects Incode’s broader mission to restore trust online. By combining full-stack technology ownership with continuous model innovation, Incode is building adaptive defenses that evolve alongside emerging threats. In an era where reality can be convincingly forged, restoring trust is not optional - it is foundational to the future of digital interaction.
