Spaces:
Sleeping
Sleeping
File size: 3,908 Bytes
a055f15 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import gradio as gr
from transformers import pipeline
# ---- Model registry (all are image-classification on HF) ----
# Sources:
# - prithivMLmods/Deep-Fake-Detector-v2-Model (labels: Realism / Deepfake)
# - Wvolf/ViT_Deepfake_Detection (real/fake)
# - yermandy/deepfake-detection (CLIP-encoder baseline for deepfake)
MODEL_REGISTRY = {
"ViT Deepfake v2 (Prithiv)": "prithivMLmods/Deep-Fake-Detector-v2-Model",
"ViT Deepfake (Wvolf)": "Wvolf/ViT_Deepfake_Detection",
"CLIP Deepfake (yermandy)": "yermandy/deepfake-detection",
}
_pipes = {}
def _get_pipe(model_id: str):
if model_id not in _pipes:
_pipes[model_id] = pipeline("image-classification",
model=model_id,
device_map="auto")
return _pipes[model_id]
def _fake_real_probs(result):
# result: list[{'label': str, 'score': float}]
fake, real = 0.0, 0.0
for r in result:
lbl = r["label"].strip().lower()
s = float(r["score"])
if ("fake" in lbl) or ("deepfake" in lbl) or ("ai" in lbl):
fake = max(fake, s)
if ("real" in lbl) or ("realism" in lbl) or ("authentic" in lbl):
real = max(real, s)
if fake==0.0 and real==0.0:
# fallback: take top-1 and mirror
top = max(result, key=lambda x: x["score"])
is_fake = ("fake" in top["label"].lower()) or ("deepfake" in top["label"].lower()) or ("ai" in top["label"].lower())
if is_fake:
fake, real = float(top["score"]), 1.0 - float(top["score"])
else:
real, fake = float(top["score"]), 1.0 - float(top["score"])
# normalize to sum<=1 if both present
s = fake + real
if s > 1.0 and s > 0:
fake, real = fake/s, real/s
return fake, real
def predict(img, model_name, ensemble, top_k, threshold):
model_ids = list(MODEL_REGISTRY.values()) if ensemble else [MODEL_REGISTRY[model_name]]
agg_fake, agg_real, rows = [], [], []
for mid in model_ids:
pipe = _get_pipe(mid)
out = pipe(img, top_k=top_k)
f, r = _fake_real_probs(out)
agg_fake.append(f); agg_real.append(r)
for item in out:
rows.append([mid, item["label"], float(item["score"])])
fake_prob = sum(agg_fake)/len(agg_fake)
real_prob = sum(agg_real)/len(agg_real)
pred = "FAKE" if fake_prob >= threshold else "REAL"
# aggregate chart (Label expects {label: score})
chart = {"FAKE": float(fake_prob), "REAL": float(real_prob)}
# top-k table
rows = sorted(rows, key=lambda x: x[2], reverse=True)[: top_k * len(model_ids)]
return pred, chart, rows
with gr.Blocks(theme="soft") as demo:
gr.Markdown("# 🔎 Deepfake Detector\nChoose a model or use an **Ensemble** for a more robust score.")
with gr.Row():
with gr.Column(scale=3):
img = gr.Image(type="pil", label="Upload image (face works best)")
with gr.Accordion("Settings", open=False):
model_name = gr.Dropdown(list(MODEL_REGISTRY.keys()),
value="ViT Deepfake v2 (Prithiv)",
label="Backbone")
ensemble = gr.Checkbox(label="Ensemble (use all models)", value=False)
top_k = gr.Slider(1, 5, value=3, step=1, label="Top-k per model")
threshold = gr.Slider(0.1, 0.9, value=0.5, step=0.01, label="FAKE threshold")
btn = gr.Button("Predict", variant="primary")
with gr.Column(scale=2):
pred = gr.Label(label="Prediction (FAKE vs REAL)")
chart = gr.Label(label="Aggregated probabilities")
table = gr.Dataframe(headers=["model", "label", "score"], wrap=True)
btn.click(predict, [img, model_name, ensemble, top_k, threshold], [pred, chart, table])
if __name__ == "__main__":
demo.launch()
|