Detecting Deepfakes and Proxy Test Takers: A Guide for Engineering Leaders
Implement robust defenses against modern fraud tactics using liveness and behavioral telemetry.
"In hiring, the cost of a single fraud attempt can spiral into a $50K nightmare—act before it’s too late."Back to all posts
Your AI model just hallucinated in production, costing $50K in customer refunds. This is not just a tech mishap; it's a glaring vulnerability that bad actors are eager to exploit. As deepfakes and proxy test-takers grow increasingly sophisticated, the stakes are higher than ever. A single misstep can lead to fraudulent
candidates slipping through the cracks, leading to wasted resources and potential brand damage. Engineering leaders must act swiftly to implement robust defenses against these modern fraud tactics.
For engineering leaders, the importance of detecting deepfakes and proxy test-takers cannot be overstated. The implications stretch far beyond immediate financial loss; they can tarnish your organization's reputation and erode trust with stakeholders. As hiring processes become more automated, the risk of manipulation—
through deepfakes or proxy test-takers—escalates. By addressing these vulnerabilities head-on, you not only safeguard your hiring process but also enhance the integrity of your organization.
Step 1: Implement Liveness Checks Begin by integrating liveness detection into your candidate verification process. This can be achieved through real-time video analysis that verifies whether the candidate is present and responding to prompts. Look for capture anomalies such as unusual head movements or delays in video
responses—these can signal potential deepfakes or recorded videos. Step 2: Utilize Device Fingerprinting Incorporate device fingerprinting to establish a unique profile for each candidate's device. This includes tracking browser settings, operating system details, and IP addresses. Regularly check for discrepancies—if
a candidate's device suddenly changes, it could indicate proxy usage. Step 3: Monitor Behavioral Telemetry Implement behavioral telemetry to track patterns in candidate interactions. Look for mismatches in voice and ID during verification, unexpected pauses in responses, or changes in typing speed during assessments.
Key takeaways
- Implement liveness checks to ensure genuine interactions.
- Utilize device fingerprints to track user behavior.
- Monitor behavioral telemetry for anomalies in candidate performance.
Implementation checklist
- Establish liveness checks in your verification process.
- Integrate device fingerprinting into your ATS.
- Implement behavioral telemetry monitoring for each candidate.
Questions we hear from teams
- What are the signs of a deepfake during video interviews?
- Look for capture anomalies, such as unusual head movements, unnatural lighting, or delays in responses.
- How can device fingerprinting help in fraud detection?
- Device fingerprinting establishes a unique profile for each candidate's device, allowing you to track inconsistencies that may indicate proxy usage.
- What metrics should I monitor for behavioral telemetry?
- Focus on voice-ID mismatches, typing speed variations, and response patterns to identify anomalies.
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