arthash

v0.5.0 safe
3.0
Low Risk

Placeholder-image hash family — DCT / CIRCLE / TRIANGLE / SQUARE / RECT / ROTATED_RECT / PIXEL modes share a unified Codec API. PyO3 binding to arthash-rs.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all checks with no network calls, shell executions, obfuscations, or credential harvesting attempts. The only notable concern is the metadata risk due to the maintainer's account status.

  • No network calls detected
  • No shell execution detected
  • Maintainer has a new or inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: No shell execution detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, indicating potential unreliability.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • Test runner config found: conftest.py
  • 11 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2543 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 35 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 60 commits in Jannchie/arthash
  • Single author but highly active (60 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Jannchie/arthash appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with arthash 
Create a Python-based image comparison tool using the 'arthash' package. This tool will allow users to upload two images and compare them based on various hashing algorithms provided by the 'arthash' package such as DCT, CIRCLE, TRIANGLE, SQUARE, RECT, ROTATED_RECT, and PIXEL. The goal is to determine if the images are visually similar or identical.

### Steps to Create the Tool:
1. **Setup Project Environment**: Initialize a new Python project and install the necessary packages including 'arthash'.
2. **User Interface Design**: Develop a simple user interface where users can upload two images. Consider using a web framework like Flask for simplicity.
3. **Image Processing**: Implement functions to process the uploaded images using the 'arthash' package. Each mode of 'arthash' should be explored to understand its output and uniqueness.
4. **Comparison Logic**: Write code to compare the hashes of the two images using each mode available in 'arthash'. Determine a threshold value above which the images can be considered similar.
5. **Results Display**: Display the results of the comparison back to the user through the UI. Include visual feedback such as color coding to indicate similarity levels.
6. **Error Handling**: Ensure robust error handling for scenarios like invalid file types or network issues during uploads.
7. **Testing & Optimization**: Test the application thoroughly under different conditions and optimize the comparison logic for better performance.

### Suggested Features:
- Allow users to select multiple images and batch compare them.
- Provide a histogram or graph showing the similarity score across different hashing modes.
- Integrate with a cloud storage service for large-scale comparisons.
- Offer an option to download the report of the comparison.

💬 Discussion Feed

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