Merge pull request #166 from ComplexData-MILA/feature/openfake-media-backend

Update OpenFake media backend integration to new Compute Canada API

Author: dorsafsallami

app/core/config.py

@@ -42,7 +42,7 @@ class Settings(BaseSettings):
     DEBUG: bool = False
 
     # For OpenFake
-    OPENFAKE_API_URL: str = "https://complexdatalab-openfakedemo.hf.space/api/predict"
+    OPENFAKE_API_URL: str = "https://deepfake-detector.ai4.institute/api/predict"
     MEDIA_VERIFICATION_ENABLED: bool = True
 
     def __init__(self, **kwargs):

app/services/openfake_service.py

@@ -1,11 +1,12 @@
 import os
+from typing import List, Optional
 
 import httpx
 from fastapi import HTTPException, UploadFile
 
 OPENFAKE_API_URL = os.getenv(
     "OPENFAKE_API_URL",
-    "https://complexdatalab-openfakedemo.hf.space/api/predict",
+    "https://deepfake-detector.ai4.institute/api/predict",
 )
 
 ALLOWED_TYPES = {
@@ -20,6 +21,33 @@
 }
 
 
+def _as_float(value: object, default: float = 0.0) -> float:
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _verdict_from_score(p_fake: float) -> str:
+    if p_fake >= 0.75:
+        return "Likely fake"
+    if p_fake >= 0.45:
+        return "Uncertain"
+    return "Likely real"
+
+
+def _frame_probs_from_frames(frames: object) -> Optional[List[float]]:
+    if not isinstance(frames, list):
+        return None
+
+    frame_probs = []
+    for frame in frames:
+        if isinstance(frame, dict) and "p_fake" in frame:
+            frame_probs.append(_as_float(frame.get("p_fake")))
+
+    return frame_probs or None
+
+
 async def verify_media_with_openfake(file: UploadFile) -> dict:
     if file.content_type not in ALLOWED_TYPES:
         raise HTTPException(
@@ -29,7 +57,6 @@ async def verify_media_with_openfake(file: UploadFile) -> dict:
 
     await file.seek(0)
 
-    # This forwards the uploaded file object to OpenFake
     files = {
         "file": (
             file.filename,
@@ -45,30 +72,38 @@ async def verify_media_with_openfake(file: UploadFile) -> dict:
         )
 
     if response.status_code != 200:
+        detail = "OpenFake detector failed"
+        try:
+            error_data = response.json()
+            detail = error_data.get("detail") or error_data.get("error") or detail
+        except ValueError:
+            pass
+
         raise HTTPException(
             status_code=502,
-            detail="OpenFake detector failed",
+            detail=detail,
         )
 
     data = response.json()
 
-    p_fake = float(data.get("p_fake", 0))
-    reliability = float(data.get("reliability", 1 - p_fake))
-
-    if p_fake >= 0.75:
-        verdict = "Likely fake"
-    elif p_fake >= 0.45:
-        verdict = "Uncertain"
-    else:
-        verdict = "Likely real"
+    p_fake = _as_float(data.get("p_fake"))
+    reliability = _as_float(data.get("reliability"), 1 - p_fake)
+    frame_probs = data.get("frame_probs") or _frame_probs_from_frames(data.get("frames"))
 
     return {
         "media_type": data.get("media_type"),
         "p_fake": p_fake,
+        "p_real": data.get("p_real"),
+        "p_localized": data.get("p_localized"),
+        "p_full_synthetic": data.get("p_full_synthetic"),
+        "p_fake_max": data.get("p_fake_max"),
+        "generators": data.get("generators", []),
         "reliability": reliability,
         "reliability_score": round(reliability * 100),
-        "verdict": verdict,
+        "verdict": _verdict_from_score(p_fake),
         "n_frames": data.get("n_frames"),
-        "frame_probs": data.get("frame_probs"),
-        "explanation": (f"The detector estimates a {round(p_fake * 100)}% probability " f"that this media is fake."),
+        "frame_probs": frame_probs,
+        "frames": data.get("frames"),
+        "mask": data.get("mask"),
+        "explanation": (f"The detector estimates a {round(p_fake * 100)}% probability that this media is fake."),
     }