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Professional-grade digital image forensics and manipulation detection in the browser.

Forensically is a sophisticated, browser-based suite of digital image forensic tools designed for deep analysis of image integrity and authenticity. Developed by Jonas Wagner, the platform operates entirely on the client-side, ensuring that sensitive images never leave the user's local environment, which is a critical requirement for investigative journalism and legal discovery in 2026. The technical architecture leverages advanced JavaScript processing to perform computationally intensive tasks such as Error Level Analysis (ELA), Principal Component Analysis (PCA), and Clone Detection. In the current landscape of AI-generated content and sophisticated deepfakes, Forensically serves as a vital first-line defense for fact-checkers, insurance adjusters, and law enforcement. It enables the identification of localized compression differences, pixel-level inconsistencies, and hidden metadata that reveal the history of an image's manipulation. Its open-access model and technical transparency have solidified its position as the industry standard for transparent, verifiable image verification without the overhead of enterprise SaaS subscriptions.
Forensically is a sophisticated, browser-based suite of digital image forensic tools designed for deep analysis of image integrity and authenticity.
Explore all tools that specialize in error level analysis. This domain focus ensures Forensically delivers optimized results for this specific requirement.
Resaves the image at a known compression rate and calculates the difference, highlighting areas with different compression levels.
Uses a block-matching algorithm to find similar regions within the image that may indicate 'healing' or 'cloning' brush usage.
Decomposes the image into its principal components to isolate noise and color variations.
Extracts GPS coordinates from EXIF data and plots them directly onto an interactive map interface.
Applies a reverse-denoising filter to isolate the underlying sensor noise of the camera.
Analyzes the direction and intensity of light hitting objects within the frame.
Extracts the original embedded thumbnail to see if it differs from the current full-size image.
Access the Forensically web interface via 29a.ch/photo-forensics.
Drag and drop the target image (JPG/PNG) into the browser window.
Select 'Magnifier' to inspect pixel-level artifacts and high-contrast edges.
Execute 'Clone Detection' to identify duplicated regions within the same frame.
Run 'Error Level Analysis' (ELA) to detect varying levels of compression.
Use 'Noise Analysis' to check for inconsistencies in sensor noise patterns across the image.
Analyze 'Principal Component Analysis' (PCA) to find anomalies in color distribution.
Open 'Metadata' to view EXIF, XMP, and IPTC headers for camera/GPS data.
Utilize 'Thumbnail Analysis' to compare embedded thumbnails with the main image.
Use the 'Luminance Gradient' tool to detect inconsistent light sources on subjects.
All Set
Ready to go
Verified feedback from other users.
"Users highly value the tool for its privacy-first local processing and professional-grade algorithms provided for free. Journalists and OSINT researchers consider it an essential utility."
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