Local-first image forgery detection with Error Level Analysis (ELA), copy-move detection, and perceptual hashing — for macOS.
Scan thousands of images and instantly find exact duplicates grouped by content hash
Extract all embedded images from PDF documents with format detection and metadata
Purpose-built for academic integrity officers, journal editors, and forensic auditors.
Multi-stage pipeline using SHA-256, perceptual hashing, and visual comparison. Finds duplicates even after resizing, cropping, or re-compression.
Extract all embedded images from PDF documents with a single click. Supports JPEG and PNG with lossless extraction.
Detect tampering by analyzing JPEG compression artifacts. Manipulated regions appear as bright spots in the ELA visualization.
Identify copy-pasted regions within a single image using block-based DCT matching with visual overlay highlighting.
Compare any two images with slider, side-by-side, and diff views. RMSE distortion metric with instant similar/different verdict.
Auto-generate professional audit reports with thumbnails of flagged duplicates and similarity scores. Ready to share with stakeholders.
Choose a folder of images or a PDF document to analyze.
Picspect runs multi-stage analysis: hashing, perceptual comparison, and visual verification.
Browse exact duplicates, similar pairs, and run ELA or copy-move detection on flagged images.
Generate a PDF audit report with all findings, ready for review or submission.
Trusted by professionals who need to verify image authenticity.
Universities and journal editors use Picspect to screen submitted papers for duplicated or manipulated figures — catching fraud before publication.
Claims adjusters detect reused or altered photos across multiple claims — preventing payouts on fabricated or exaggerated damage.
Auditors verify the authenticity of photographic evidence in financial reports, compliance documents, and legal proceedings.
Start for free. Upgrade when you need more.