Verify Sources Before You Publish
Deepfake video volume is estimated to be growing at roughly 900% a year, and incidents like the Arup deepfake video-call fraud show how convincing synthetic media has become. Aiscern's multi-modal pipeline screens text, image, audio, and video submissions before they reach print.
No credit card required · Free tier always available
Synthetic Media Outpaces Manual Checks
The AI content problem is getting harder to solve. Here's what professionals in media & journalism face every day.
AI-generated press releases flood editorial inboxes
PR agencies and bad actors use LLMs to mass-generate press releases. Journalists spend time verifying content that may be entirely AI-fabricated.
Deepfake images are used in disinformation campaigns
Synthetic images are shared as photographic evidence in political, social, and conflict contexts, putting newsrooms at risk of publishing misinformation.
Voice clone audio is indistinguishable from real
AI-synthesized audio clips mimic political figures, executives, and witnesses. Traditional verification is no longer sufficient.
Viral deepfake videos spread before verification can catch up
The detection window is narrow. By the time manual verification completes, synthetic content has already been widely shared.
How It Fits Your Workflow
Ingest Tip or Asset
Submit an image, video, audio clip, or text tip for screening.
Run Multi-Modal Scan
The relevant ensemble analyzes the asset for synthetic-media markers.
Flag With Confidence Score
Editorial gets a confidence score before the piece goes to print.
How Aiscern Solves It
Our ensemble-based detection pipeline combines 8+ specialized models with a confidence threshold system.Learn about our methodology →
AI Text Detection
Ensemble RoBERTa + Binoculars analysis on press releases, reports, and submitted articles with ≥96% AUC.
Deepfake Image Detection
ViT-based classifier with pixel-level integrity analysis. Detects GAN-generated and diffusion model images.
Video Deepfake Detection
Frame-level analysis combined with NVIDIA NIM deepfake models for facial manipulation detection.
Audio Clone Detection
wav2vec2-based voice analysis against ASVspoof benchmarks — flags synthetic speech with 92% recall.
Forensic Reports
Exportable reports with model confidence breakdown, scan ID, and timestamp for editorial documentation.
API for Newsroom Workflows
Integrate detection directly into CMS submission pipelines. Auto-flag content before it reaches editorial review.
ℹ️ Accuracy varies by content type and model generation date. Results are probabilistic — use alongside human judgment.See full benchmarks →
Real-World Use Cases
User-Submitted Media Screening
Challenge
A digital newsroom receives thousands of tips and images during a breaking news event.
Action
The API scans each image at submission time.
Fact-Checker Text Verification
Challenge
A fact-checking desk receives a viral op-ed with suspiciously uniform prose.
Action
Sentence-level analysis identifies which sections read as AI-generated.
Source Audio Authentication
Challenge
An investigative journalist receives an audio clip purportedly of a government official.
Action
The audio pipeline analyzes spectral characteristics for cloning artifacts.
How Aiscern Compares to Manual fact-checking
| Feature | Aiscern | Manual fact-checking |
|---|---|---|
| Time per asset | Under 15 sec | Minutes to hours |
| Video deepfake detection | Case-by-case | |
| Voice clone detection | ||
| CMS API integration | N/A | |
| Consistent across shifts |
[QUOTE TEXT HERE]”
Frequently Asked Questions
How fast can Aiscern analyze submitted content during breaking news?
Text detection returns results in under 2 seconds for most submissions. Image and audio detection complete within 5–15 seconds. Video analysis is longer — typically 30–90 seconds per minute of footage.
Does image detection work on screenshots and compressed social media images?
Yes, though compression artifacts can reduce accuracy. We recommend submitting the highest-quality version available. Our pixel-integrity layer is designed to work on JPEG-compressed images.
What is the false positive rate for image detection?
Our image ensemble achieves approximately 3% false positive rate on benchmark datasets (14-layer ensemble, AUC 0.98). Real-world rates vary by image type. We always recommend human editorial review of flagged content.
Can Aiscern detect deepfake video of political figures?
Our video pipeline analyzes temporal consistency and facial artifacts. It performs best on face-swap and lip-sync deepfakes. Highly sophisticated full-body synthesis may reduce accuracy — see our /benchmarks page for dataset-specific results.
Is there a newsroom bulk pricing plan?
Yes. Enterprise and Team plans include volume-based pricing, dedicated API limits, and priority support. Contact us at /enterprise for newsroom-specific agreements.
Ready to detect AI content in media & journalism?
Start with a free account — no credit card, no commitment. Upgrade when you need more scans.