import os
import numpy as np
import pandas as pd
from datetime import datetime

# STEP 1: Check GitHub Repository Activity
# Fraudulent papers often have suspicious commit histories
def check_github_activity(repo_url):
    """
    Red flags:
    - Single massive commit with all code
    - No recent activity after publication
    - Issues/PRs disabled or deleted
    """
    print(f"Checking: {repo_url}")
    # In practice: Use GitHub API or web scraping
    return "SUSPICIOUS" if "no recent commits" else "OK"

# STEP 2: Verify Random Seed Manipulation
# Hardcoded seeds = identical "random" results
def verify_randomness(code_path):
    """
    Look for:
    - torch.manual_seed(42) in multiple places
    - np.random.seed(42) without variation
    - Same seed across all experiments
    """
    suspicious_lines = []
    with open(code_path, 'r') as f:
        for i, line in enumerate(f, 1):
            if 'manual_seed(' in line or '.seed(' in line:
                suspicious_lines.append(f"Line {i}: {line.strip()}")
    return suspicious_lines

# STEP 3: Test Result Consistency
# Real models vary slightly; frauds output identical results
def test_result_consistency(model, test_data, runs=10):
    """
    Run multiple times with different seeds.
    Fraudulent models will output identical results every time.
    """
    results = []
    for i in range(runs):
        np.random.seed(i)  # Different seed each run
        result = model.predict(test_data)
        results.append(result)
    
    # Check if all results are identical
    all_identical = all(np.array_equal(results[0], r) for r in results)
    return "FRAUD DETECTED" if all_identical else "RESULTS VARY (GOOD)"

# Usage example:
if __name__ == "__main__":
    print("Run these checks on any suspicious AI paper's code.")
    print("1. check_github_activity('github.com/suspicious/repo')")
    print("2. verify_randomness('model_code.py')")
    print("3. test_result_consistency(their_model, test_data)")

The 'Oops, All Fraud!' Paper

So there's this paper about detecting scientific fraud—irony alert—that got published in a real conference. The authors claimed their fancy new model was amazing at spotting shady research. The only problem? Their own research was shadier than a palm tree at noon.

When curious folks went to check their GitHub repo (because that's what you do when results look too good to be true), they found something hilarious: the model was basically a magic eight ball that always gave the same answer. They'd hardcoded the random seed, making every run identical, and their 'model' had collapsed into giving one output. It's like claiming you invented a revolutionary new car, but when people look under the hood, there's just a hamster on a wheel.

When Your GitHub History Becomes a Mystery

Here's where it gets even better. When someone politely raised an issue on their repository pointing out these... let's call them 'creative interpretations of scientific method,' the authors didn't respond with data or explanations. They did what any confident researcher would do: they deleted the entire repository. Poof! Gone faster than my willpower near free pizza.

This is the academic version of 'delete your browser history.' If your research can't survive someone looking at your code, maybe the problem isn't the code—it's the research. The paper still exists in the conference proceedings, sitting there like that one awkward family photo everyone pretends doesn't exist.

The Punchline You Already Saw Coming

What's truly funny about all this is the paper was about detecting fraud in science. It's like writing a bestselling book about honesty while shoplifting the paper it's printed on. The universe has a sense of humor, and sometimes it writes better punchlines than we ever could.

The lesson here isn't just about checking GitHub repos (though definitely do that). It's about the fact that in the age of AI hype, some people will try to pass off digital smoke and mirrors as actual magic. And when they get caught? Let's just say their disappearing act is more impressive than their research.