🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate suspicion due to potential obfuscation and the use of shell commands, although it does not appear to engage in network calls or credential harvesting.

Unusual code patterns indicating possible obfuscation
Use of shell commands and subprocess execution

Per-check LLM notes

Network: No network calls detected, which is normal for a library focused on local image processing.
Shell: The use of shell commands and subprocess execution may indicate legitimate functionality but also raises concerns about potential security risks such as command injection.
Obfuscation: The code snippets show unusual patterns that may indicate obfuscation to hide the true functionality of the code.
Credentials: No suspicious patterns for credential harvesting were detected.
Metadata: The maintainer has only one package and there are non-HTTPS links, but no clear signs of malicious activity.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

True).features.to(opt.device).eval() cnn_normalization_mean = torch.tensor([0.485, 0.456,
input=True): self.GAN.eval() if hist_batch is None or image_batch is None:
tent_file=None): self.GAN.eval() if hist_batch is None: batch = next(self.loader
.load(load_from) model.GAN.eval() histblock = RGBuvHistBlock(insz=hist_insz, h=hist_bin,
pretrained=True).features[:4].eval()) blocks.append( torchvision.models.vgg16(pretrai

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

mpling_method == 'BGU': os.system('BGU.exe ' f'"{input_image_name}" '
_run(command): return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() sha = 'N/A'
'rev-parse', 'HEAD']) subprocess.check_output(['git', 'diff'], cwd=cwd) diff = _run(['git', 'diff-
'LC_ALL'] = 'C' out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: potechius.com

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

Non-HTTPS external link: http://dx.doi.org/10.1111/j.1467-8659.2009.01566.x
Non-HTTPS external link: http://dx.doi.org/10.1117/12.477378

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Herbert Potechius" 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 ColorTransferLib

Create a photo editing mini-app using Python's 'ColorTransferLib' package. The app should allow users to upload an image and apply various color and style transfer effects to it. Additionally, include a feature that allows users to compare the original and transformed images side-by-side, providing visual feedback on the changes made.

Step-by-Step Requirements:
1. Implement a user interface where users can upload an image file from their device.
2. Use the 'ColorTransferLib' package to apply different color and style transfer algorithms to the uploaded image. Ensure you have at least three distinct effects available, such as 'Van Gogh', 'Monet', and 'Cubism'.
3. Integrate a slider or dropdown menu in the UI to allow users to adjust the intensity of the applied effect.
4. Utilize the IQA (Image Quality Assessment) metrics provided by 'ColorTransferLib' to display quantitative feedback about the quality of the transformed image compared to the original. This could be shown as a score or a brief qualitative description.
5. Develop a feature that splits the screen into two halves, showing the original image on one side and the transformed image on the other, allowing for direct comparison.
6. Add a download button that allows users to save the edited image after applying the desired effects.
7. Ensure the application is responsive and user-friendly, with clear instructions and feedback messages throughout the process.