Repository Analysis

image-rs/image

Encoding and decoding images in Rust

4.7 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of image-rs/image, a Rust project with 5,818 GitHub stars. SynthScan v2.0 examined 45,477 lines of code across 142 source files, recording 206 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 4.7 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

4.7
Adjusted Score
4.7
Raw Score
100%
Time Factor
2026-07-12
Last Push
5.8K
Stars
Rust
Language
45.5K
Lines of Code
142
Files
206
Pattern Hits
2026-07-14
Scan Date
0.01
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 1MEDIUM 2LOW 203

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 206 distinct pattern matches across 4 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Over-Commented Block203 hits · 199 pts
SeverityFileLineSnippetContext
LOWtests/limits.rs1//! Test enforcement of size and memory limits for various decoding APIs.COMMENT
LOWsrc/io.rs41COMMENT
LOWsrc/error.rs1//! Contains detailed error representation.COMMENT
LOWsrc/error.rs21/// The generic error type for image operations.COMMENT
LOWsrc/error.rs41COMMENT
LOWsrc/error.rs141}COMMENT
LOWsrc/error.rs161}COMMENT
LOWsrc/metadata.rs21COMMENT
LOWsrc/metadata.rs81 Self::Rotate270 => 8,COMMENT
LOWsrc/color.rs101/// Another purpose is to advise users of a rough estimate of the accuracy and effort of theCOMMENT
LOWsrc/color.rs261COMMENT
LOWsrc/lib.rs1//! # OverviewCOMMENT
LOWsrc/lib.rs21//! # fn main() -> Result<(), image::ImageError> {COMMENT
LOWsrc/lib.rs41//! let mut bytes: Vec<u8> = Vec::new();COMMENT
LOWsrc/lib.rs61//! * [`GenericImage`] trait for a mutable image buffer.COMMENT
LOWsrc/lib.rs81//! # Ok(())COMMENT
LOWsrc/lib.rs101//! # Ok(())COMMENT
LOWsrc/lib.rs181COMMENT
LOWsrc/lib.rs201/// | `gif` | GIF |COMMENT
LOWsrc/lib.rs221/// _wrappers_, not direct re-exports, in either of the following cases:COMMENT
LOWsrc/lib.rs281// <https://github.com/GuillaumeGomez/doc-comment>COMMENT
LOWsrc/hooks.rs121///COMMENT
LOWsrc/hooks.rs141///COMMENT
LOWsrc/primitive_sealed.rs61impl BgraSwizzle for isize {}COMMENT
LOWsrc/primitive_sealed.rs81 /// Returns the nearest value of `Self` to `value`.COMMENT
LOWsrc/traits.rs361 // fact that `Self::Subpixel` is used for `TransformableSubpixel` from the bound onCOMMENT
LOWsrc/traits.rs441 /// A string that can help to interpret the meaning each channelCOMMENT
LOWsrc/traits.rs461 Self::Subpixel::DEFAULT_MAX_VALUECOMMENT
LOWsrc/animation.rs41 /// Delay between the frames in millisecondsCOMMENT
LOWsrc/animation.rs141COMMENT
LOWsrc/animation.rs161 /// assert_eq!(delay_10ms, Delay::from_numer_denom_ms(30, 3));COMMENT
LOWsrc/animation.rs181 /// use std::time::Duration;COMMENT
LOWsrc/codecs/gif.rs1//! Decoding of GIF ImagesCOMMENT
LOWsrc/codecs/tiff.rs201 UnsupportedErrorKind::GenericFeature(COMMENT
LOWsrc/codecs/openexr.rs1//! Decoding of OpenEXR (.exr) ImagesCOMMENT
LOWsrc/codecs/png.rs161 .map(|x| f64::from(x.into_scaled()) / 100_000.0))COMMENT
LOWsrc/codecs/png.rs701#[derive(Clone, Copy, Debug, Eq, PartialEq)]COMMENT
LOWsrc/codecs/png.rs721#[derive(Clone, Copy, Debug, Eq, PartialEq)]COMMENT
LOWsrc/codecs/farbfeld.rs1//! Decoding of farbfeld imagesCOMMENT
LOWsrc/codecs/hdr/decoder.rs201 /// Create a new decoder with the given spec compliance mode.COMMENT
LOWsrc/codecs/hdr/decoder.rs501pub struct HdrMetadata {COMMENT
LOWsrc/codecs/hdr/mod.rs1//! Decoding of Radiance HDR ImagesCOMMENT
LOWsrc/codecs/webp/encoder.rs1//! Encoding of WebP images.COMMENT
LOWsrc/codecs/avif/yuv.rs261/// * `image`: see [YuvGrayImage]COMMENT
LOWsrc/codecs/avif/yuv.rs281/// * `range`: see [YuvIntensityRange]COMMENT
LOWsrc/codecs/avif/yuv.rs301///COMMENT
LOWsrc/codecs/avif/yuv.rs1121COMMENT
LOWsrc/codecs/avif/yuv.rs1141/// # ArgumentsCOMMENT
LOWsrc/codecs/avif/yuv.rs1161/// * `range`: see [YuvIntensityRange]COMMENT
LOWsrc/codecs/avif/mod.rs1//! Encoding of AVIF images.COMMENT
LOWsrc/codecs/jpeg/encoder.rs21COMMENT
LOWsrc/codecs/jpeg/encoder.rs61///COMMENT
LOWsrc/codecs/jpeg/encoder.rs181COMMENT
LOWsrc/codecs/bmp/decoder.rs361COMMENT
LOWsrc/codecs/bmp/decoder.rs441 #[default]COMMENT
LOWsrc/codecs/bmp/decoder.rs461 /// bitmask bytes or palette). This is the minimum valid data_offset.COMMENT
LOWsrc/codecs/bmp/decoder.rs481 Checkpoint { row: u32, x: u32, stream_pos: u64 },COMMENT
LOWsrc/codecs/bmp/decoder.rs1161 /// headers. If it returns an `UnexpectedEof` error, you can retry on theCOMMENT
LOWsrc/codecs/bmp/decoder.rs1181 /// // Phase 1: Read metadata (with retry on UnexpectedEof)COMMENT
LOWsrc/codecs/bmp/decoder.rs1461 self.reader.seek(SeekFrom::Start(icc.profile_offset))?;COMMENT
143 more matches not shown…
Synthetic Comment Markers1 hit · 8 pts
SeverityFileLineSnippetContext
HIGHsrc/animation.rs8/// An implementation dependent iterator, reading the frames as requestedCOMMENT
Slop Phrases1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUM.github/workflows/rust.yml70 # an emulated mips system. NOTE: you can also use this approach to test forCOMMENT
AI Slop Vocabulary1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMsrc/io/decoder.rs6/// The interface for `image` to utilize in reading image files.COMMENT