Vesion Fonctionnelle

check_video_authenticity.bat

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+@echo off
+call .venv\Scripts\activate.bat
+python "%~n0.py" %*
+

check_video_authenticity.py

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+import os
+import sys
+import requests
+import json
+import tkinter as tk
+from tkinter import filedialog
+from rich.console import Console
+from rich.progress import Progress, SpinnerColumn, TextColumn
+from rich.panel import Panel
+from rich.table import Table
+from rich import print
+
+# --- CONFIGURATION ---
+# TODO: Replace with your actual Sightengine API credentials
+API_USER = "1020608869"
+API_SECRET = "M7WF4mAXwSs2gs869LC9PecvYCBQyBSz"
+
+API_ENDPOINT = "https://api.sightengine.com/1.0/video/check-sync.json"
+
+console = Console()
+
+def select_video_file():
+    """Opens a file dialog to select a video file using Tkinter."""
+    root = tk.Tk()
+    root.withdraw() # Hide the main window
+    
+    file_path = filedialog.askopenfilename(
+        title="Select a video file to analyze",
+        filetypes=[
+            ("Video Files", "*.mp4 *.mov *.avi *.mkv *.webm *.flv *.wmv"), 
+            ("All Files", "*.*")
+        ]
+    )
+    return file_path
+
+def check_video(file_path):
+    """Checks the video using Sightengine API."""
+    if not os.path.exists(file_path):
+        console.print(f"[bold red]Error:[/bold red] File not found: {file_path}")
+        return
+
+    params = {
+        'models': 'genai',
+        'api_user': API_USER,
+        'api_secret': API_SECRET
+    }
+
+    files = {
+        'media': open(file_path, 'rb')
+    }
+
+    try:
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            transient=True
+        ) as progress:
+            progress.add_task(description="Uploading and analyzing video...", total=None)
+            response = requests.post(API_ENDPOINT, files=files, data=params)
+        
+        response_data = json.loads(response.text)
+        
+        if response_data['status'] == 'success':
+            display_results(file_path, response_data)
+        else:
+            error_msg = response_data.get('error', {}).get('message', 'Unknown error')
+            console.print(f"[bold red]API Error:[/bold red] {error_msg}")
+
+    except Exception as e:
+        console.print(f"[bold red]System Error:[/bold red] {e}")
+    finally:
+        files['media'].close()
+
+def display_results(file_path, data):
+    """Displays the analysis results using Rich."""
+    
+    # Calculate aggregate score (average of all frames for simplicity in this demo)
+    frames = data.get('data', {}).get('frames', [])
+    if not frames:
+        console.print("[yellow]No frames analyzed.[/yellow]")
+        return
+
+    total_ai_score = 0
+    count = 0
+    
+    # Create detailed table for frames (showing first few or high scores)
+    table = Table(title="Frame Analysis (Sample)")
+    table.add_column("Time (s)", justify="right", style="cyan", no_wrap=True)
+    table.add_column("AI Probability", justify="right", style="magenta")
+    table.add_column("Verdict", justify="center")
+
+    max_ai_score = 0
+
+    for frame in frames:
+        ai_score = frame.get('type', {}).get('ai_generated', 0)
+        total_ai_score += ai_score
+        count += 1
+        if ai_score > max_ai_score:
+            max_ai_score = ai_score
+        
+        # Add to table if it's a significant score or just to show some data
+        # Showing only frames with > 0.1 score or every 10th frame to keep it clean if many frames
+        if ai_score > 0.5 or count % 10 == 1:
+             verdict = "[red]FAKE[/red]" if ai_score > 0.8 else ("[yellow]SUSPICIOUS[/yellow]" if ai_score > 0.4 else "[green]REAL[/green]")
+             # Assuming 1 frame per second roughly? API documentation says 'position' is likely offset
+             position = frame.get('info', {}).get('position', 0)
+             # Provide string formatting for score
+             table.add_row(str(position), f"{ai_score:.2%}", verdict)
+
+    avg_ai_score = total_ai_score / count if count else 0
+    
+    # Final Verdict Panel
+    final_verdict = "UNCERTAIN"
+    color = "yellow"
+    if max_ai_score > 0.85:
+        final_verdict = "AI GENERATED"
+        color = "red"
+    elif max_ai_score < 0.2:
+        final_verdict = "AUTHENTIC"
+        color = "green"
+    
+    request_info = data.get('request', {})
+    req_id = request_info.get('id', 'N/A')
+    ops = request_info.get('operations', 0)
+    
+    panel_content = f"""
+    [bold]File:[/bold] {os.path.basename(file_path)}
+    [bold]Request ID:[/bold] {req_id}
+    [bold]Operations Cost:[/bold] {ops}
+    
+    [bold]Max AI Score:[/bold] {max_ai_score:.2%}
+    [bold]Average AI Score:[/bold] {avg_ai_score:.2%}
+    
+    [bold size=20 style={color}]VERDICT: {final_verdict}[/bold size=20 style={color}]
+    """
+    
+    console.print(Panel(panel_content, title="Analysis Result", expand=False))
+    console.print(table)
+
+def main():
+    if API_USER == "CHANGE_ME" or API_SECRET == "CHANGE_ME":
+        console.print("[bold red]Configuration Error:[/bold red] Please set API_USER and API_SECRET in the script.")
+        return
+
+    if len(sys.argv) > 1:
+        video_path = sys.argv[1]
+    else:
+        console.print(Panel(
+            "[bold green]AI Video Authenticity Check[/bold green]\n\n"
+            "This script analyzes a video to detect AI-generated content using Sightengine.\n"
+            "Please select a video file in the window that opens...",
+            title="Welcome",
+            border_style="green"
+        ))
+        video_path = select_video_file()
+        if not video_path:
+            console.print("[yellow]No file selected. Exiting.[/yellow]")
+            return
+
+    console.print(f"[blue]Analyzing:[/blue] {video_path}")
+    check_video(video_path)
+
+if __name__ == "__main__":
+    try:
+        main()
+    finally:
+        input("Press Enter to continue...")