Deepfake and disinformation: cyber-physical threats

16 January 2026

Table of contents

How deepfakes work: generative AI, models, datasets, and pipelines
Detection techniques: watermarking, forensics, behavioral analysis
Practical guide: open-source deepfake detection (audio and video)
Awareness program and simulations: from “seeing” to “recognizing”

Deepfakes are no longer laboratory curiosities they are tangible tools within everyone’s reach, capable of generating videos, audio, and synthetic images that mimic real voices and faces with uncanny precision.

These contents deceive employees, customers, suppliers, journalists, and even authentication systems. Within the broader ecosystem of disinformation, such technologies act as amplifiers of deception, accelerating social-engineering attacks, financial frauds, and reputational crises that often spill from the digital into the physical world causing operational disruptions, panic, or tangible damage.

This article explores how deepfakes actually work (their generative AI models, datasets, and pipelines), how they are used in both legitimate and illicit contexts, the main attack vectors (from spear-phishing to CEO fraud), and their impact on reputation, operations, and crisis management. It also provides an overview of detection and mitigationtechniques watermarking, forensic analysis, behavioral analysis, and user awareness and closes with a practical guide to deploying an open-source library for detecting audio and video deepfakes, complete with working code examples and a discussion of ethical and regulatory considerations.

How deepfakes work: generative AI, models, datasets, and pipelines

Common architectures

Autoencoders / Encoder-Decoder models compress and reconstruct facial or vocal data through a latent space.
GANs (Generative Adversarial Networks) pair a generator and discriminator that train each other to produce realistic content.
Diffusion models dominant in image generation and increasingly in video start from noise and iteratively denoise it into coherent content.
Text-to-Speech (TTS) and Voice Conversion models clone tone and prosody to synthesize audio using mere seconds of recorded speech.
NeRF / GAIT / Body models reconstruct body pose and motion for full-figure falsifications.

Data sourcing

Datasets are often scraped from public sources interviews, podcasts, social videos. Even a few seconds of clean recordings are enough for a credible voice clone. This ease of access fuels both commercial applications (e.g., dubbing, avatars) and malicious misuse (impersonation, extortion).

Synthetic pipeline

Data Collection
Extracting faces and audio tracks.
Pre-processing
Normalization, noise removal, facial alignment.
Training/Fine-tuning
Adapting the model to the target.
Generation
Rendering images, video, or audio.
Post-production
Lip-syncing, tone matching, compression.
Distribution
Deploying the product through social media, email, or VoIP calls in disinformation campaigns or frauds.

Examples
Fraud, Spear-Phishing, and Next-Gen Social Engineering

CEO fraud 2.0

Classic CEO fraud relied on forged emails; the 2.0 version leverages deepfaked video or audio of an executive ordering urgent payments or data transfers. The persuasive realism of voice and face bypasses psychological defenses especially when combined with contextual spear-phishing (real project names, amounts, and urgency).

Supply chain and procurement

A trusted supplier appears on a Zoom call: the face is familiar, the voice matches, the background looks right. They request a change in banking details. The deepfake convincingly closes the trap.

Extortion and reputation damage

Short videos or synthetic images can threaten executives or politicians for extortion or blackmail—or manipulate public opinion by spreading plausible fakes. Disinformation often aims not only to deceive but to sow doubt(“plausible deniability”) even about authentic footage.

Customer care and KYC fraud

Voice clones can exploit IVR or live support systems to pass security checks based on voice recognition, successfully impersonating high-value clients.

Implications: reputation, operations, and corporate crisis

Reputation
A viral deepfake video can destroy trust in minutes, while official denials trail behind. Rebuilding credibility costs time and resources.
Operations
Payment fraud, credential leaks, or supply-chain manipulation cause direct financial loss and service disruption.
Corporate crisis
Cyber-physical incidents panic, market reactions, or employee confusion require specific playbooks and crisis communication strategies.

Detection techniques: watermarking, forensics, behavioral analysis

There’s no magic detector. You need to combine technical signals and behavioral analysis, automation, and awareness.

Watermarking and provenance

Watermarks embedded at pixel or latent level can certify synthetic origin—but attackers rarely include them.
Emerging standards like C2PA manifests help track media provenance and authenticity chains.

Forensic media analysis

Photometric inconsistencies
Unnatural reflections or shadows.
Compression artifacts
Non-standard DCT patterns.
Lip-sync issues
Millisecond-level desynchronization.
Blink rate anomalies
Repetitive or absent eye motion.
Audio traces
Vocoder artifacts, frequency band gaps.

Behavioral analysis

Channel verification
Never approve payments or data sharing based on one audio/video channel only.
Contextual red flags
Off-hours requests, sudden IBAN changes, “urgent and confidential” tone.
Reputation signals
New accounts, few followers, abnormal posting patterns.

Mitigation: from technical controls to awareness

Multi-channel verification for sensitive actions.
Financial controls
Dual authorization, whitelist accounts, transfer limits.
Email and chat hardening
Banners, DMARC/DKIM/SPF, sandboxing.
Identity gates: MFA, out-of-band verification, digital signatures.
Detection tools integrated into SOC workflows (see guide below).
Awareness training on spear-phishing, CEO fraud, and urgency manipulation.
Crisis playbooks defining who decides, when to freeze payments, and how to communicate.

Practical guide: open-source deepfake detection (audio and video)

Below is a compact Python pipeline for SOC/DFIR teams, combining audio and video analysis.

Objective: classify audio clips as “genuine” vs “synthetic” using spectrograms + a lightweight classifier; and in parallel verify vocal identity with speaker verification (embeddings).

1) Audio detection (voice clone / TTS)

Step A — Feature extraction

import librosa, numpy as np

def log_mel_spectrogram(wav_path, sr=16000, n_mels=64):

    y, sr = librosa.load(wav_path, sr=sr, mono=True)

    # normalizzazione semplice

    y = librosa.util.normalize(y)

    S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, n_fft=1024, hop_length=256)

    S_db = librosa.power_to_db(S, ref=np.max)

    # ridimensiona a blocchi uniformi (es. 3s)

    target_frames = int(3 * sr / 256)

    if S_db.shape[1] < target_frames:

        # pad a destra

        pad = target_frames - S_db.shape[1]

        S_db = np.pad(S_db, ((0,0),(0,pad)), mode='constant')

    else:

        S_db = S_db[:, :target_frames]

    return (S_db - S_db.mean()) / (S_db.std() + 1e-6)

Step B — Lightweight CNN Classifier (PyTorch)

import torch, torch.nn as nn, torch.nn.functional as F

class SmallCNN(nn.Module):

    def __init__(self):

        super().__init__()

        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)

        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)

        self.pool = nn.MaxPool2d(2,2)

        self.fc1 = nn.Linear(32*16*32, 64)  # dipende dalle dimensioni finali

        self.fc2 = nn.Linear(64, 2)

    def forward(self, x):

        x = self.pool(F.relu(self.conv1(x)))

        x = self.pool(F.relu(self.conv2(x)))

        x = x.view(x.size(0), -1)

        x = F.relu(self.fc1(x))

        return self.fc2(x)  # logits

def predict_audio(model, S_db):

    with torch.no_grad():

        x = torch.tensor(S_db, dtype=torch.float32).unsqueeze(0).unsqueeze(0)  # (1,1,mels,frames)

        logits = model(x)

        prob = torch.softmax(logits, dim=-1)[0,1].item()  # prob sintetico

        return prob

Step C — Speaker verification (embedding cosine)
Compare the clip with a ‘reference’ voice of the CEO (archived): if the similarity is low but the clip ‘sounds’ synthetic, it’s likely a voice clone.

from sklearn.metrics.pairwise import cosine_similarity

def cosine(a, b):

    return float(cosine_similarity(a.reshape(1,-1), b.reshape(1,-1))[0,0])

# ipotizza funzioni get_speaker_embedding(...) basate su modelli open (es. x-vector/wav2vec)

emb_ref = get_speaker_embedding('/path/ceo_ref.wav')     # enrollment voce reale

emb_test = get_speaker_embedding('/path/suspect.wav')    # clip sospetta

sim = cosine(emb_ref, emb_test)

# regole semplici:

# se sim < 0.6 e prob_sintetico > 0.7  -> forte sospetto deepfake

Audio operational policy

Conservative threshold (e.g., prob_synthetic > 0.7 or sim < 0.6).
If positive, block the request and trigger out-of-band verification.

2) Video capture (face-swap, lip-sync)

Step A — Frame extraction + face crop
Use FFmpeg to extract 8–16 evenly spaced frames; then use a face detector (e.g., RetinaFace or MediaPipe) for consistent cropping.

ffmpeg -i suspect.mp4 -vf "fps=4,scale=640:-1" frames/out_%03d.png -y

Step B — Features and classifier
Two complex but effective strategies together:

Lip-sync check: aligns audio → phonemes (forced alignment) and compares with lip movement (landmarks).
Forensics CNN on face patches (artifacts, blink rate, texture inconsistencies).

Pseudo-pipeline:

def extract_landmarks(img_path): ...

def forced_alignment(audio_path): ...

def lip_sync_score(landmarks_seq, phoneme_seq): ...

def forensic_cnn_score(face_crops): ...

# Output combinato con regole/ensemble

score = 0.6*forensic_cnn + 0.4*(1 - lip_sync_mismatch)

Step C — Metadata corroboration

Container reading: bitrate, GOP, watermark / manifest if present.
Heuristics: inconsistent blink frequencies, light/shadow mismatch.

3) Orchestration, logging, and “light” MLOps

Inference gateway
Microservice that receives media, produces “synthetic-prob” scores.
Logging (JSON)
Scores, thresholds, reasons (transparency for audit).
Feedback loop
Confirmed incidents feed retraining (hard negatives).
Privacy
Anonymize internal datasets, apply retention rules.

4) SOC/IT decisions and flows

Block/Allow/Review
Three states. If high risk, block and notify.
Ticketing
Attach evidence (frames, spectrograms) to Jira/ServiceNow.
Awareness
Notify the target department with a targeted training snippet (the deception pattern just observed).

Awareness program and simulations: from “seeing” to “recognizing”

Awareness works when it is concrete:

Comparative gallery
Authentic clips vs deepfakes with indication of signals (eyes, lips, background noise).
Simulations
Spear-phishing campaigns that include audio and video; measure reporting rate.
Two-confirmation rule
No payment or sensitive data for inputs received only via audio/video.
“CEO unavailable” playbook
What to do when the attacker invokes urgency/confidentiality.

Incident response for deepfake and disinformation events

Triage
Isolate the source, hash and preserve originals.
Forensics
Analyze frames, spectrograms, scores.
Containment
Freeze accounts, revoke compromised access.
Communication
Concise, factual statements—avoid amplification.
Legal/PR
Takedown requests, brand defense.
Lessons learned
Update models, policies, and workflows.

Ethical and regulatory considerations

Responsibility
Developers must embed watermarking and traceability; organizations using generative AI must apply risk assessments.
Regulation
Frameworks now require transparency for synthetic media and provenance standards (e.g., EU AI Act, C2PA).
Awareness and transparency
Always disclose synthetic content in corporate communication or marketing.
Fairness
Detection models must avoid demographic bias (balanced datasets).

Operational checklist

Verified supplier accounts and dual-signature policies.
“No single channel” rule for sensitive approvals.
Deploy watermark/provenance on internal media.
Integrate audio/video detectors into SOC tools.
Quarterly awareness campaigns and spear-phishing drills.
Disinformation crisis playbooks (PR + legal).
Periodic audit of models and datasets.
Behavioral telemetry to detect anomalies.

Integration with Zero-Trust and Identity-First Architectures

Continuous checks
Multimedia content is not identity. Use MFA, device posture signals, risk-based policies.
High-risk sessions
When an action is required via audio/video, push an additional challenge (digital signature).
Minimal privileges
Reduced limits and timeframes lessen the impact of a fraudulent order.
Correlated telemetry
Connect media indicators with account anomalies, geolocation, unusual timings.

Reference architectures and design patterns

Media Vetting Service
Centralized forensic and watermark checks.
Content Provenance Gateway
Validates and stamps media sources.
Trust Labels
Visible authenticity indicators (“verified source”, “synthetic content”).
Context graphs
Correlate sender, channel, and timing anomalies.

Metrics and KPIs

A call arrives with audio of the ‘CFO’ requesting a transfer.
The audio gateway calculates synthetic_prob=0.81, voice similarity sim=0.55.
The ERP blocks the request and launches an out-of-band verification on the approvals app.
The user declines → SOC ticket with attached forensic analysis; post-event training for the finance team.

Example: urgent payment validation flow

Call arrives with “CFO” voice requesting urgent transfer.
Audio gateway flags synthetic probability = 0.81; similarity = 0.55.
ERP blocks transaction and triggers out-of-band verification.
User denies it → SOC ticket opened with forensic report; targeted training follows.

In conclusion

Deepfakes and disinformation form a new hybrid cyber-physical threat vector: digital manipulations leading to tangible harm in reputation, operations, and even public safety. Defense requires a layered approach: watermarking and forensic analysis for content; behavioral verification and “no-single-channel” policies for processes; ongoing awareness programs to train staff; and concrete technical measures like the open-source deepfake-detection pipeline demonstrated above.

Building resilience means combining technology, governance, and ethics under clear regulations. Only by merging these dimensions can organizations drastically reduce their exposure to spear-phishing, CEO fraud, and disinformation, restoring trust and control in an era where seeing or hearing is no longer believing.

Frequent asked questions

What is a deepfake?
A synthetic video, audio, or image produced by generative AI to impersonate real people.
Why are deepfakes so convincing?
They use advanced models (GAN, diffusion) trained on rich datasets of human features.
What’s the main corporate risk?
CEO fraud, spear-phishing, and reputational or operational damage.
Does watermarking solve it?
No, attackers can omit or remove it use multiple detection layers.
Can humans spot a deepfake visually?
Sometimes, but combining forensic and behavioral analysis is safer.
Why is awareness crucial?
Because even the best tech fails if employees trust persuasive forgeries.
What financial controls help?
Dual signatures, verified accounts, separate confirmation channels.
Can detection be automated?
Yes open-source tools can flag suspicious media in real time.
How to manage a disinformation crisis?
Preserve evidence, communicate clearly, engage PR/legal teams.
What are the ethical duties?
Declare synthetic media, prevent misuse, and educate users.