Show HN: Is this photo edited? Client-side image forensics
Recorded: Sept. 14, 2026, 5:08 p.m.
| Original | Summarized |
Is This Photo Edited? - Free Image Forensics Tool | Vajba Vajba/Image Tools·Search Check a photo for signs of editing, AI generation, hidden metadata, and compression artifacts Is This Photo Edited? Upload a photo for forensic analysis Drop a photo to analyze Select Image Load Pasting a URL fetches the image from that site to your browser. Vajba never receives the file, but the remote site will see your browser request. Downloaded reports include the Source URL, and reverse-search links pass it to those services. No photo handy? Try an example: Also useful: Overall Read Running tests... Findings are per-test. The overall read sums only suspicious evidence from Hard and Supportive tests. What this result means, and what it does not Expand all details Cancel analysis Print / save as PDF Download JSON Analyze another photo Forensic findings grouped by evidence class About AI Detection What works, what doesn't, and what this app actually does Verify a Content Credential Home Say hi at [click to reveal email] |
The Vajba tool functions as an in-browser image forensics utility designed to detect signs of editing, artificial intelligence generation, hidden metadata, and compression artifacts within digital images. The process involves analyzing the file's intrinsic byte structure directly within the user's browser, ensuring that the file itself is never uploaded. The analysis draws upon examining metadata, encoder signatures, embedded provenance claims, pixel statistics, and various structural clues to gather evidence. However, the findings generated by the tool are intended to provide clues for investigation rather than offering definitive proof of an image's authenticity or manipulation. The reliability and interpretation of the findings are governed by the evidence class. Hard evidence represents verifiable facts derived directly from the file's data, while supportive evidence serves as hints that require external corroboration. The tool applies a system where correlated findings from a single source are down-weighted to prevent exaggerated verdicts. A significant aspect of the tool’s forensic capability centers on cryptographic content provenance, specifically utilizing Content Credentials (C2PA). This method is presented as the most reliable means of determining how an image was produced. When image generation or editing software, such as Photoshop, Firefly, DALL-E, Leica, or Sony tools, applies a signature to their output, a verifier can use C2PA to confirm the identity of the signer and the specific edits recorded. The system performs rigorous cryptographic checks, including verifying the COSE_Sign1 signature using algorithms like ECDSA and RSA-PSS, ensuring that all assertions made by the signer hash correctly to the claimed values, and verifying the asset hash binding to confirm that the image bytes precisely match the signed SHA-256 hash. Furthermore, the process involves walking the certificate chain against the official C2PA trust list to validate the subject and public key anchors. For images that lack this Content Credential data, the detection of AI-generated content becomes inherently less reliable, particularly after the image has undergone resizing, recompression, or other processing, as metadata is susceptible to being stripped or forged. In such scenarios, the tool’s algorithmic detection capabilities may be unreliable. Consequently, the text suggests that reverse image searching is often a more robust method for assessing authenticity than relying solely on these algorithmic tools. The overall forensic findings are consolidated by summing evidence from the hard and supportive tests performed on the file. |