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import streamlit as st
from transformers import AutoImageProcessor, AutoModelForImageClassification
import cv2
import torch
import numpy as np
import tempfile
image_processor = AutoImageProcessor.from_pretrained(
'ashish-001/deepfake-detection-using-ViT')
model = AutoModelForImageClassification.from_pretrained(
'ashish-001/deepfake-detection-using-ViT')
def classify_frame(frame):
inputs = image_processor(images=frame, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.sigmoid(logits)
pred = torch.argmax(logits, dim=1).item()
lab = 'Real' if pred == 1 else 'Fake'
confidence, _ = torch.max(probs, dim=1)
return f"{lab}::{format(confidence.item(), '.2f')}"
st.title("Deepfake detector")
uploaded_file = st.file_uploader(
"Upload an image or video",
type=["jpg", "jpeg", "png", "mp4", "avi", "mov", "mkv"]
)
placeholder = st.empty()
if st.button('Detect'):
if uploaded_file is not None:
clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
mime_type = uploaded_file.type
if mime_type.startswith("image"):
file_bytes = uploaded_file.read()
np_arr = np.frombuffer(file_bytes, np.uint8)
image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = clf.detectMultiScale(
gray, scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(image_rgb, (x, y), (x+w, y+h), (0, 0, 255), 2)
face = image_rgb[y:y + h, x:x + w]
img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
label = classify_frame(img)
new_frame = cv2.putText(
image_rgb, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
st.image(new_frame)
elif mime_type.startswith('video'):
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
temp_file.write(uploaded_file.read())
temp_video_path = temp_file.name
cap = cv2.VideoCapture(temp_video_path)
if not cap.isOpened():
st.error("Error: Cannot open video file.")
else:
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = clf.detectMultiScale(
gray, scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(
frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
face = frame[y:y + h, x:x + w]
img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
label = classify_frame(img)
frame = cv2.putText(
frame, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
placeholder.image(frame)
cap.release()
else:
st.write("Please upload an image or video")
if st.button('Use Example Video'):
clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture("Sample.mp4")
if not cap.isOpened():
st.error("Error: Cannot open video file.")
else:
st.write(f"Video credits: 'Deep Fakes' Are Becoming More Realistic Thanks To New Technology. Link:https://www.youtube.com/watch?v=CDMVaQOvtxU")
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = clf.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(
frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
face = frame[y:y + h, x:x + w]
img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
label = classify_frame(img)
frame = cv2.putText(
frame, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
placeholder.image(frame)
cap.release()
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