Deepfakes (Voice and Video): DefendEdge Executive Brief Risks, Tools, Detection, and Client-Facing Guidance

Executive Summary

Voice and video deepfakes are actively used to impersonate senior leaders and push payments, credentials, and malware in real time. Recent events include an attempted impersonation of WPP executives that staff blocked, and North Korea–aligned BlueNoroff using deepfaked Zoom calls to deliver macOS malware. The FBI’s Internet Crime Complaint Center (IC3) warned on May 15, 2025, that malicious actors are using AI voice and text to impersonate senior U.S. officials. This brief translates those developments into concrete controls and client‑ready messaging. [1][2][3][4]

1.  How Modern Deepfakes are Made, Core Methods and Tools

Modern deepfakes use encoder and decoder networks as well as generative adversarial networks to synthesize or manipulate faces and speech. For voice, text‑to‑speech systems with neural vocoders clone timbre and cadence from short samples. Popular tools include open‑source face‑swap projects and commercial voice and video synthetic media services. The barrier to entry keeps dropping, which expands attacker access to these capabilities. [5][6]

2.  Real‑world Examples of Abuse

• CEO Voice Impersonation Fraud (2019): An energy subsidiary executive authorized a wire transfer of about 243,000 USD after a phone call that convincingly mimicked the timbre and cadence of the parent-company CEO. Attacker goal: BEC-style cashouts through urgency and authority. Organizational lesson: Finance controls that rely on voice familiarity are brittle. Require directory-verified callbacks and multi-approver release even when the request seems routine. [7]
• WPP Attempted Deepfake Scam (2024): Attackers staged a fabricated meeting workflow, including an AI voice clone of senior leadership, calendar invites, and pretexts across channels. Loss was averted because employees escalated anomalies and used a known-good callback. Lesson: layered social proofs can be forged. Treat cross-channel consistency as a weak signal unless it routes through your directory and identity stack. [2]
• Senior U.S. Officials Impersonation (2025): IC3 warned of smishing and vishing campaigns leveraging AI-generated voices and texts to target officials and their contacts, seeking credentials, sensitive documents, and money movement. Lesson: when an adversary can mass-produce credible authority signals, verification must move to possession-based and directory-anchored controls, not “recognize the voice.” [1][4]
• DPRK BlueNoroff Operations (2025): Campaigns used deepfaked Zoom calls, Telegram outreach, and tailored lures to deliver macOS backdoors to Web3 and fintech employees. Voice and video were used to sustain rapport until payload execution or wallet access. Lesson: treat all first-contact executive outreach as untrusted. Default to sandboxed collaboration, restrict file exchange, and require directory callbacks before any credential or wallet operations. [3][8][9]

3.  Detection Techniques and Limitations

• Pixel and Temporal Forensics: Frame-level and sequence-level models inspect blending seams, warping, inconsistent lighting, motion jitter, and audio-video sync drift. Best used as triage signals for analyst review and to prioritize incident response. [10][11]
• Biological Signal Analysis: Some systems infer pulse-like skin-tone changes and micro-blink patterns to detect computer-generated faces. Performance degrades with compression, filters, low light, heavy makeup, and high motion. Treat it as a supporting feature, not a gate. [12]Audio anti‑spoofing: Countermeasures flag synthetic, converted, or replayed speech in speaker-verification flows using spectral and phase cues. Effective against known synthesis pipelines but sensitive to channel noise and replay quality. Pair with directory callbacks for material approvals. [13]
• Content Provenance: Signing media at creation time and carrying edit history through C2PA Content Credentials allows verifiers to check source, tools, and changes. Strongest for your own outbound media, brand assets, and press materials. It will not authenticate third-party or live calls unless the ecosystem participates. [14][15]
• Limits: Detectors overfit to known methods, lose accuracy on unseen techniques, and degrade in the wild. Scores shift with codecs, bandwidth, and device capture. Use detectors to route events to humans, never as sole decision makers for payments, credential resets, or policy exceptions. Track precision, recall, and false positive cost in production and retrain with your own artifact corpus. [10][11]

4.  Prevention, Mitigation, and CTI Operational Guidance Policy Controls:

• Require out-of-band verification for sensitive requests. Only call back using a directory-verified number surfaced by your corporate directory or SSO profile. No approvals by voicemail.
• Enforce dual approvers and cooling-off windows on high-value transfers, vendor bank changes, gift card purchases, and wallet moves.
• Run role-specific deepfake drills in red-team tabletop exercises for executives, finance, legal, HR, and PR. Include phone, video, chat, and mixed-channel scenarios.

Technical Controls:

• Integrate media anomaly detectors in high-risk channels. Use scores for triage and analyst prompts in ticketing, not as auto-deny.
• Adopt C2PA Content Credentials for your official media, investor videos, and press kits. Publish verification how-to on your site.
• Log and retain suspicious media artifacts with hashes, call metadata, and transcripts to support forensics, hunting, and model retraining.

CTI Operations

• Collection: Track TTPs for voice and video synthesis kits, initial-access lures, and payment pretexts tied to your exec names, brands, and suppliers. Monitor paste sites, Telegram, and talent platforms for impostor listings.
• Requirements: PIRs around executive impersonation, vendor bank-change fraud, Web3 wallet targeting, and BlueNoroff-like tradecraft.
• Hunting: Query for rapid supplier bank-detail updates, urgent off-hours payment requests, new external meeting organizers claiming senior titles, and anomalous first-contact DMs to EAs.
• Response playbooks: Include callback scripts, challenge phrases, and immediate containment steps for finance and EA queues.
• Metrics: Time-to-callback, percent of high-risk requests blocked pre-payment, detector assist rate, and drill pass rate by function.

Client Playbook: Channel Controls

Channel	Primary controls	Notes
Live meetings (Teams, Zoom)	Known‑good callback before any sensitive decision; challenge‑response in meeting; disable external join by default; record and archive meetings that request money or credentials	Be cautious with new profiles that claim executive status. Verify identity via directory callback before sharing links or passwords.
Voice calls and voicemails	No approvals via voicemail; callback to a directory‑verified number; optional speaker verification with anti‑spoofing	Do not trust caller ID. Provide finance and EA staff with standard refusal language and escalation paths.
Messaging apps (Signal, WhatsApp, Telegram)	Treat first contact as untrusted; require directory callback; restrict link‑clicking; isolate and detonate attachments	Use URL rewriting and safe previews where available. Block unknown file types and enforce read-only until verified.
Email	Strengthen BEC defenses; flag urgent payment language; enforce multi‑approver flows	Flag payment urgency and gift card language. Auto-route vendor bank changes to a dedicated review queue.
Public media and press assets	Sign official media with Content Credentials; keep originals under change control	Educate audiences, investors, and media on how to validate your signatures and where to find canonical assets.

References

1. FBI IC3. “Senior US Officials Impersonated in Malicious Messaging Campaign.” May 15, 2025. https://www.ic3.gov/PSA/2025/PSA250515
2. The Guardian. “CEO of world’s biggest ad firm targeted by deepfake scam.” May 10, 2024. https://www.theguardian.com/technology/article/2024/may/10/ceo-wpp-deepfake-scam
3. The Hacker News. “BlueNoroff Deepfake Zoom Scam Hits Crypto Employee with macOS Backdoor Malware.” June 19, 2025. https://thehackernews.com/2025/06/bluenoroff-deepfake-zoom-scam-hits.html
4. Reuters. “Malicious actors using AI to pose as senior US officials, FBI says.” May 15, 2025. https://www.reuters.com/world/us/malicious-actors-using-ai-pose-senior-us-officials-fbi-says-2025-05-15/
5. Rössler, A. et al. “FaceForensics++.” ICCV 2019. https://openaccess.thecvf.com/content_ICCV_2019/papers/Rossler_FaceForen sics_Learning_to_Detect_Manipulated_Facial_Images_ICCV_2019_paper.pd f
6. Dolhansky, B. et al. “The DeepFake Detection Challenge (DFDC) Dataset.” 2020.
7. Forbes. “A voice deepfake was used to scam a CEO out of $243,000.” Sept 3, 2019. https://www.forbes.com/sites/jessedamiani/2019/09/03/a-voice-deepfake-was-used-to-scam-a-ceo-out-of-243000/
8. Kaspersky GReAT. “BlueNoroff’s GhostCall and GhostHire campaigns.” Oct 28, 2025. https://securelist.com/bluenoroff-apt-campaigns-ghostcall-and-ghosthire/117842/
9. Kaspersky Press. “BlueNoroff targets executives on Windows and macOS using AI‑driven tools.” Oct 27, 2025. https://www.kaspersky.com/about/press-releases/kaspersky-bluenoroff-targets-executives-on-windows-and-macos-using-ai-driven-tools
10. FaceForensics++ dataset GitHub. https://github.com/ondyari/FaceForensics
11. DFDC dataset. https://ai.meta.com/datasets/dfdc/
12. Intel FakeCatcher coverage. https://www.lifewire.com/intels-new-deepfake-detection-platform-spots-fakes-using-our-blood-6828532
13. ASVspoof 2021. https://www.asvspoof.org/index2021.html
14. C2PA overview. https://c2pa.org/
15. DoD Chief Digital and AI Office. “Strengthening Multimedia Integrity in the Generative AI Era: Content Credentials.” Jan 16, 2025.