Asmaad/DeefFakeDetection_using_XceptionNet

1import streamlit as st2import cv23import tempfile4import numpy as np5import os6from PIL import Image7import gc8import tensorflow9import tensorflow as tf10from tensorflow.keras.applications import Xception11from tensorflow.keras.models import Model12from tensorflow.keras.layers import Dense, GlobalAveragePooling2D  # or ConvNeXtBase, depending on your model13 14 15 16# Set page config17st.set_page_config(page_title="DeepFake Detector", layout="centered", initial_sidebar_state="collapsed")18 19# Minimal dark mode style20st.markdown("""21<style>22    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600&display=swap');23 24    html, body, .stApp {25        background-color: #0e0e0e;26        color: #ffffff;27        font-family: 'Inter', sans-serif;28        padding-top: 1rem;29        padding-bottom: 1rem;30    }31 32    .block-container {33        padding: 2rem 2rem;34    }35 36    h1, h2, h3, .stMarkdown, .stFileUploader, .stVideo, .stImage, .stButton, .stProgress, .score-box {37        margin-top: 1.5rem !important;38        margin-bottom: 1.5rem !important;39    }40 41    h1, h2, h3 {42        color: #fefefe;43        font-weight: 600;44        border-left: 4px solid #00e5ff;45        padding-left: 10px;46    }47 48    .stButton>button {49        background-color: #00e5ff;50        color: black;51        border-radius: 10px;52        padding: 0.6em 1.2em;53        font-weight: 600;54        border: none;55        transition: 0.3s;56        margin-top: 1rem;57        margin-bottom: 1rem;58    }59 60    .stButton>button:hover {61        background-color: #00bcd4;62        transform: scale(1.05);63    }64 65    .stFileUploader {66        background-color: #1e1e1e;67        padding: 1em;68        border-radius: 12px;69        border: 1px dashed #444;70    }71 72    .stVideo, .stImage > img {73        border-radius: 8px;74        box-shadow: 0 0 10px rgba(0,0,0,0.5);75        margin-top: 1rem;76        margin-bottom: 1rem;77    }78 79    .stProgress > div > div > div > div {80        background-color: #00e5ff;81    }82 83    .score-box {84        background-color: #1e1e1e;85        padding: 1em;86        margin-top: 1.5rem;87        margin-bottom: 1.5rem;88        border-radius: 10px;89        border-left: 4px solid #00e5ff;90        font-size: 1.1rem;91    }92 93</style>94""", unsafe_allow_html=True)95 96 97 98# Logo99# st.markdown("""100# <a href="https://www.intel.com/content/www/us/en/research/fakecatcher.html" target="_blank">101#     <img src="https://cdn-icons-png.flaticon.com/512/10471/10471465.png" width="100">102# </a>103# """, unsafe_allow_html=True)104 105# App title & subtitle106st.title("🕵️‍♂️ DeepFake Detector")107st.markdown("Upload a video and detect deepfakes using an AI-based model — powered by Xception!")108 109st.markdown("""110<div class='animated-desc'>111Analyze your video content for potential deepfake alterations using cutting-edge frame-by-frame AI detection.112</div>113""", unsafe_allow_html=True)114 115# Upload file116uploaded_file = st.file_uploader("📤 Upload a video (MP4, MOV, AVI)", type=["mp4", "mov", "avi"])117@st.cache_resource118def load_model():119    weights_path = "src/Xception_ft.weights.h5"120    base_model = Xception(weights=None, include_top=False, input_shape=(299, 299, 3))121    x = GlobalAveragePooling2D()(base_model.output)122    x = Dense(1, activation='sigmoid')(x)123    model = Model(inputs=base_model.input, outputs=x)124    model.load_weights(weights_path)125    return model126 127model = load_model()128 129 130# Real detector function131def real_fake_detector(frame: np.ndarray) -> float:132    resized = tf.image.resize(frame, (299, 299)) / 255.0133    resized = tf.expand_dims(resized, axis=0)134    prediction = model.predict(resized, verbose=0)[0][0]135    return float(prediction)136 137# Main logic138if uploaded_file:139    tfile = tempfile.NamedTemporaryFile(delete=False)140    tfile.write(uploaded_file.read())141    video_path = tfile.name142 143    # Show uploaded video144    st.video(uploaded_file)145 146    st.info("🧠 Extracting frames and analyzing with local model...")147    cap = cv2.VideoCapture(video_path)148    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))149    selected_frames = []150    fake_scores = []151    progress = st.progress(0)152 153    for i in range(frame_count):154        ret, frame = cap.read()155        if not ret:156            break157 158        if i % 20 == 0:159            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)160            selected_frames.append(frame_rgb)161            score = real_fake_detector(frame_rgb)162            fake_scores.append(score)163 164        progress.progress(min((i + 1) / frame_count, 1.0))165 166    cap.release()167 168    # Display a few sample frames169    st.subheader("📷 Sample Extracted Frames")170    cols = st.columns(min(len(selected_frames), 5))171    for idx, img in enumerate(selected_frames[:5]):172        with cols[idx]:173            st.image(Image.fromarray(img), use_column_width=True)174 175    avg_score = np.mean(fake_scores)176    st.markdown("---")177    st.subheader("📊 DeepFake Analysis Result")178 179    with st.container():180        st.markdown(f"""181            <div class='score-box'>182            <strong>Average Fake Score:</strong> {avg_score:.2f}<br>183            <strong>Confidence Level:</strong> {'⚠ High' if avg_score > 0.75 else '✅ Moderate'}184            </div>185        """, unsafe_allow_html=True)186    187        if avg_score > 0.5:188            st.error("🔍 The video is likely **DeepFake**.")189        else:190            st.success("🎉 The video appears **Authentic**.")191