Deepfake Defense 2026: Detect, Defend & Defeat Threats

2026深度伪造防御实战课

掌握GAN检测、语音克隆识别与EfficientNet取证，通过10个动手实验室构建企业级深度伪造防御体系，应对40亿美元AI诈骗威胁。

Published 4/2026
Created by Armaan Sidana
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 10 Lectures ( 1h 42m ) | Size: 1.1 GB

Detect, Defend & Defeat AI Fakes: GANs, Voice Cloning, EfficientNet Forensics, C2PA & 10 Hands-On Labs

What you’ll learn
✓ Build a complete deepfake detection pipeline
✓ Fine-tune and harden AI detection models
✓ Implement enterprise deepfake defense frameworks
✓ Investigate suspected deepfakes using OSINT methodology

Requirements
● Basic Python knowledge
● Command line comfort
● A computer with 8GB+ RAM
● Basic cybersecurity awareness

Description
Deepfakes are rapidly emerging as one of the most significant cyber threats of 2026. Fraud losses are projected to reach $40 billion by 2027, with a single AI-generated video call already costing one company $25 million. Meanwhile, Deepfake-as-a-Service platforms can produce highly convincing fakes for as little as $20. If your organization does not yet have a detection and defense strategy, it is already at risk.

This course provides a complete, end-to-end toolkit—covering everything from how deepfakes are created to how they can be detected, investigated, and mitigated at enterprise scale.

What sets this course apart?

This is not a passive, lecture-based experience. You will build real systems through 10 hands-on labs, including

• Image classification models

• Frame-by-frame video analysis pipelines

• Audio voice-clone detection systems

• C2PA content provenance implementation

• Invisible watermarking techniques

• EfficientNet fine-tuning

• Grad-CAM forensic visualization

• Adversarial attack and defense strategies

• OSINT-based investigations

• A full capstone detection system achieving an AUC of 0.983

You will begin by mastering the attacker’s toolkit—GANs, diffusion models, voice cloning (XTTS-v2, ElevenLabs), lip-sync systems like Wav2Lip, real-time face swapping pipelines, and the economics behind Deepfake-as-a-Service. Understanding how deepfakes are built is key to understanding how they fail.

Building layered defenses

You will then design and implement advanced detection and defense mechanisms, including

• Frequency-domain analysis and GAN fingerprinting

• EfficientNet-B4 transfer learning on FaceForensics++ (AUC 0.971 in 15 epochs)

• Grad-CAM explainability heatmaps suitable for forensic reporting

• Adversarial hardening against FGSM and PGD attacks

• Multimodal fusion of visual, audio, temporal, and metadata signals (AUC 0.998)

• Lip-sync verification using SyncNet and behavioral biometrics like blink patterns

• Metadata and EXIF forensic analysis

• C2PA content provenance with ECDSA P-384 signatures

• Robust invisible watermarking (DWT-DCT) resilient to compression and re-encoding

Enterprise-ready defense strategy

Beyond technical detection, the course covers full-spectrum enterprise defense, including

• STRIDE threat modeling

• Business Email Compromise (BEC 2.0) attack scenarios

• Multi-Factor Identity Verification (MFIV) protocols

• Zero-trust integration for platforms like Teams and Zoom

• Employee awareness and training programs

• A six-phase incident response framework

• Vendor evaluation across leading solutions (Hive, Sensity, Azure, Pindrop)

Real-world investigation skills

You will also develop practical OSINT and forensic investigation capabilities, including

• Keyframe extraction using InVID

• Reverse image and video searches (TinEye, Yandex)

• Analysis of real-world deepfake cases from Slovakia, the United States, and Pakistan

• End-to-end forensic reporting with proper chain-of-custody documentation

Who should take this course?

This course is designed for

• Security professionals

• Digital forensics analysts

• Machine learning engineers

• Journalists and fact-checkers

• Anyone responsible for protecting information integrity

Basic Python and command-line knowledge are recommended. All machine learning concepts are explained from first principles.

What you will achieve

By the end of this course, you will have

• A production-ready deepfake detection API

• A custom-trained, adversarially hardened EfficientNet model

• A complete enterprise defense playbook

• Professional-grade OSINT investigation skills

• A fully integrated capstone detection system combining all components

The attacker only needs to succeed once. You need to succeed every time.
This course ensures you are prepared.