Neutralizing AI-Generated Forgeries across
Image, Video, Audio & Text.
DeepAntiForgery.ai는 이미지, 영상, 음성, 텍스트 전 영역의
AI 위조·조작을 탐지하는 멀티모달 딥테크 포렌식 플랫폼입니다.
실시간 진위 판별로 개인·기업·정부를 보호합니다.
Multimodal DeepScan™ Engine
Forensics-Grade Reports
Ready for Platforms · Banks · Governments
Try DeepScan™ Sandbox
데모용 샘플 업로드 영역입니다. (현재는 UI 전용, 실제 업로드 없음)
Sandbox
Image / VideoAudioText
⬆
Drop your file here
Image · Video · Audio · Text — up to 50MB for demo
.jpg.png.mp4.wav.txt
Demo only · No files are actually uploaded.
Production 환경에서는 안전한 저장, 감사 로그, 체인-오브-커스터디 리포트가 포함됩니다.
Core Capabilities
DeepTech Anti-Forgery Engine · Multimodal by Design
DeepAntiForgery.ai combines deep neural forensics, statistical fingerprints,
and content-aware analysis to expose AI-generated forgeries in any format.
Multimodal
Image · Video · Audio · Text
Detect forgeries across faces, scenes, cloned voices, and LLM-generated
text with a unified engine and consistent veracity score.
Forensics
Deep Forensic Fingerprints
Analyze pixel-level artifacts, temporal inconsistencies, and spectral
anomalies to distinguish synthetic content from real-world captures.
Real-Time
Low-Latency Detection
Optimized inference pipeline delivers responses in sub-second latency,
ready to plug into live platforms and transaction flows.
Explainable
Transparent Evidence Map
Highlight suspicious regions, timestamps, and acoustic bands to support
human reviewers, legal teams, and compliance workflows.
How It Works
Three-Step Anti-Forgery Protocol
From upload to veracity score, DeepAntiForgery.ai compresses a complex
forensics pipeline into three simple steps you can call via API or dashboard.
1
Ingest & Normalize
We normalize resolution, bitrate, and formatting, extract frames,
spectrograms, and text tokens while preserving evidential integrity.
2
DeepScan™ Multimodal Analysis
Cross-modal neural networks evaluate visual, acoustic, and linguistic
fingerprints to estimate how likely the content was AI-generated.
3
Veracity Score & Evidence
You receive a single veracity score, modality-wise probabilities,
and a structured evidence report ready for analysts and auditors.
DeepAntiForgery.ai Signal Stack
Different attack vectors need different defenses. Our engine stacks
complementary detectors instead of relying on a single model.
Visual Forensics
GAN fingerprints · compression ghosts · face mesh drift
Built for People, Platforms, and Public Institutions
DeepAntiForgery.ai helps individuals verify critical messages, protects
businesses from fraud, and strengthens democratic processes against
generative AI abuse.
Individuals
Verify voices, faces, and urgent requests.
Analyze suspicious voice messages, video calls, and social media posts
before reacting. Get an instant veracity score and a clear signal on
whether the content is likely synthetic.
Embed veracity checks into payment approvals, vendor onboarding,
executive communications, and contact center workflows to stop
deepfake-enabled social engineering.
Support fact-checking desks, election commissions, and law
enforcement with multimodal forensics that scale with the volume
of AI-generated disinformation.
Whether you run a fintech app, a global platform, or a public
institution, DeepAntiForgery.ai is engineered to operate in
mission-critical environments.
97.2%
Cross-modal detection accuracy (lab benchmark)
< 200 ms
Median API latency in production-like settings
4+
Modalities protected (image, video, audio, text)
24 / 7
Monitoring & integration support (enterprise)
FAQ
Frequently Asked Questions
A short overview of how DeepAntiForgery.ai fits into your stack and
what you can expect from our detection pipeline.
What types of forgeries can DeepAntiForgery.ai detect?
We focus on AI-generated forgeries across image, video, audio, and text:
deepfake faces, voice clones, GAN-generated imagery, and LLM-crafted
messages. Traditional editing artifacts can also surface through our
forensic stack, but our primary target is synthetic media.
How do you expose AI-generated content?
Our engine combines deep learning with classical forensics: we look at
inconsistencies in pixel distribution, motion, acoustics, and language
style. These signals are fused into a single veracity score with
modality-specific breakdowns.
Can I integrate this into my product via API?
Yes. You can send content via REST API and receive JSON responses with
scores, labels, and evidence markers. Enterprise plans include SDKs,
on-premise deployment options, and custom event hooks.
Is the demo on this page uploading my files?
No. The sandbox UI on this page is purely illustrative. In production
we apply encryption in transit, optional at-rest encryption, strict
retention policies, and audit logging.
Enterprise or public sector?
If you are operating a bank, large platform, or public institution,
we can adapt DeepAntiForgery.ai to your risk model, compliance
requirements, and data residency constraints.