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LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+                            Preamble
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,3 +1,80 @@
----
-license: gpl-3.0
----
+Here's a `README.md` for your project:
+
+```markdown
+# Video Manipulation Detection Using AI
+
+This project uses an AI-powered video analysis pipeline to detect potential video manipulations such as deepfakes and other visual tampering. It leverages the **DEtection TRansformer (DETR)** model for object detection and optical flow analysis to identify motion inconsistencies. The application is built using **Streamlit**, and the model processes videos to generate detailed reports on their authenticity.
+
+## Features
+- **Metadata Extraction**: Extracts and displays metadata from uploaded videos.
+- **Frame Extraction**: Extracts frames from videos at specified intervals for analysis.
+- **Object Detection**: Analyzes each frame to detect objects using the DETR model.
+- **Optical Flow Calculation**: Computes motion patterns to detect inconsistencies.
+- **Report Generation**: Compiles visual and motion analysis results into a comprehensive JSON report.
+- **Manipulation Detection**: Uses the report to assess the likelihood of video manipulation.
+
+## Technologies Used
+- **Streamlit**: Front-end web framework for creating the user interface.
+- **PyTorch**: Machine learning framework used for object detection (DETR).
+- **OpenCV**: Library for video and image processing (frame extraction, optical flow).
+- **Transformers**: Pre-trained DETR model for object detection.
+- **FFmpeg**: Tool for extracting metadata from videos and processing video files.
+
+## Installation
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/your-username/video-manipulation-detection.git
+   cd video-manipulation-detection
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the Streamlit app:
+   ```bash
+   streamlit run app.py
+   ```
+
+## How to Use
+
+1. **Upload a Video**: Go to the "Analyze Video" section and upload a `.mp4` or `.mov` video file.
+2. **Start Analysis**: Click the **"Analyze Video"** button to start processing.
+3. **View Results**: After analysis, the app will display a score and details about the potential manipulation of the video.
+
+## Report Details
+The report includes the following information:
+- **Frame Analysis**: Number of detections, maximum and average confidence of the object detection model.
+- **Motion Analysis**: Calculated optical flow values to detect motion inconsistencies.
+- **Summary Stats**: Overall detection confidence, optical flow statistics, and manipulation detection score.
+
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgments
+- [Streamlit](https://streamlit.io/)
+- [PyTorch](https://pytorch.org/)
+- [Hugging Face Transformers](https://huggingface.co/)
+- [OpenCV](https://opencv.org/)
+- [FFmpeg](https://ffmpeg.org/)
+- This project is built as part of a research and development effort to analyze and detect video manipulations.
+
+## Contact
+- **Ayush Agarwal**  
+  AIML Enthusiast, VIT Bhopal University  
+  📧 [[email protected]](mailto:[email protected])  
+  🔗 [LinkedIn](https://www.linkedin.com/in/ayush20039939)  
+  🐙 [GitHub](https://github.com/your-github)
+```
+
+### Key Sections:
+1. **Project Overview**: Describes the purpose and features of the app.
+2. **Technologies Used**: Lists the libraries and tools used in the project.
+3. **Installation**: Step-by-step guide on how to clone, install dependencies, and run the app.
+4. **How to Use**: Instructions on how users can interact with the app.
+5. **Report Details**: Describes the information generated in the analysis report.
+6. **License & Acknowledgments**: Includes the MIT License and credits to the libraries and tools used.
+
+This will help users understand the project and how to deploy and use it. Feel free to customize it further!

Realistic Universe Intro_free.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f48c2bdeae74956ab55740a60be8ab474c307478b404a8143c4d333f5bddafe
+size 2067376

final_app.py

@@ -0,0 +1,387 @@
+import cv2
+import os
+import json
+import subprocess
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from PIL import Image
+from transformers import (
+    AutoImageProcessor,
+    AutoModelForObjectDetection
+)
+import os
+import tempfile
+
+# -------------------- Configuration -------------------- #
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+FRAME_EXTRACTION_INTERVAL = 0.01  # Seconds between frame captures
+
+# -------------------- Model Loading -------------------- #
+try:
+    print("🔄 Loading visual model and processor...")
+    processor_visual = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
+    model_visual = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(DEVICE)
+    print(f"✅ Model loaded on {DEVICE} successfully!")
+except Exception as e:
+    print(f"❌ Error loading model: {e}")
+    exit()
+
+# -------------------- Metadata Extraction -------------------- #
+def extract_metadata(video_path):
+    """Extracts video metadata using FFmpeg"""
+    try:
+        cmd = ["ffprobe", "-v", "quiet", "-print_format", "json",
+               "-show_format", "-show_streams", video_path]
+        result = subprocess.run(cmd, capture_output=True, text=True)
+        return json.loads(result.stdout)
+    except Exception as e:
+        print(f"❌ Metadata extraction failed: {e}")
+        return {}
+
+# -------------------- Frame Extraction -------------------- #
+def extract_frames(video_path, output_folder="frames"):
+    """Extracts frames from video at specified interval (supports sub-second intervals)"""
+    os.makedirs(output_folder, exist_ok=True)
+    
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        print("❌ Could not open video file")
+        return 0
+
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Total frames in the video
+    total_duration = total_frames / fps  # Total duration in seconds
+    frame_count = 0
+
+    # Use a while loop for sub-second intervals
+    timestamp = 0.0
+    while timestamp <= total_duration:
+        cap.set(cv2.CAP_PROP_POS_MSEC, timestamp * 1000)  # Convert seconds to milliseconds
+        ret, frame = cap.read()
+        if ret:
+            cv2.imwrite(f"{output_folder}/frame_{frame_count:04d}.jpg", frame)
+            frame_count += 1
+        else:
+            break  # Stop if we can't read any more frames
+        
+        timestamp += FRAME_EXTRACTION_INTERVAL  # Increment by the interval
+    
+    cap.release()
+    return frame_count
+# -------------------- Optical Flow Calculation -------------------- #
+def calculate_optical_flow(frames_folder):
+    """Calculates dense optical flow between consecutive frames with validation"""
+    frame_files = sorted([f for f in os.listdir(frames_folder) if f.endswith(".jpg")])
+    flow_results = []
+    
+    # Get reference dimensions from first valid frame
+    ref_height, ref_width = None, None
+    for f in frame_files:
+        frame = cv2.imread(os.path.join(frames_folder, f))
+        if frame is not None:
+            ref_height, ref_width = frame.shape[:2]
+            break
+
+    if ref_height is None:
+        print("⚠ No valid frames found for optical flow calculation")
+        return []
+
+    prev_gray = None
+    for i in tqdm(range(len(frame_files)), desc="Calculating optical flow"):
+        current_path = os.path.join(frames_folder, frame_files[i])
+        current_frame = cv2.imread(current_path)
+
+        if current_frame is None:
+            continue
+            
+        # Ensure consistent dimensions
+        if current_frame.shape[:2] != (ref_height, ref_width):
+            current_frame = cv2.resize(current_frame, (ref_width, ref_height))
+
+        # Ensure 3-channel color format
+        if len(current_frame.shape) == 2:
+            current_frame = cv2.cvtColor(current_frame, cv2.COLOR_GRAY2BGR)
+
+        current_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
+
+        if prev_gray is not None:
+            flow = cv2.calcOpticalFlowFarneback(
+                prev_gray, current_gray, None, 
+                pyr_scale=0.5, levels=3, iterations=3,
+                winsize=15, poly_n=5, poly_sigma=1.2, flags=0
+            )
+            
+            flow_magnitude = np.sqrt(flow[...,0]*2 + flow[...,1]*2)
+            flow_results.append({
+                "max_flow": float(flow_magnitude.max()),
+                "mean_flow": float(flow_magnitude.mean())
+            })
+
+        prev_gray = current_gray
+
+    # Apply temporal smoothing
+    window_size = 5
+    smoothed_flow = []
+    for i in range(len(flow_results)):
+        start = max(0, i - window_size // 2)
+        end = min(len(flow_results), i + window_size // 2 + 1)
+        window = flow_results[start:end]
+        avg_mean = np.mean([f['mean_flow'] for f in window])
+        avg_max = np.mean([f['max_flow'] for f in window])
+        smoothed_flow.append({'mean_flow': avg_mean, 'max_flow': avg_max})
+    
+    return smoothed_flow
+
+# -------------------- Visual Analysis -------------------- #
+def detect_objects(frames_folder):
+    """Processes frames through the visual detection model"""
+    results = []
+    frame_files = sorted([f for f in os.listdir(frames_folder) if f.endswith(".jpg")])
+    
+    for frame_file in tqdm(frame_files, desc="Analyzing frames"):
+        try:
+            image = Image.open(os.path.join(frames_folder, frame_file))
+            inputs = processor_visual(images=image, return_tensors="pt").to(DEVICE)
+            
+            with torch.no_grad():
+                outputs = model_visual(**inputs)
+
+            # Process detections with lower threshold
+            target_sizes = torch.tensor([image.size[::-1]]).to(DEVICE)
+            detections = processor_visual.post_process_object_detection(
+                outputs, target_sizes=target_sizes, threshold=0.4  # Lowered from 0.7
+            )[0]
+
+            scores = detections["scores"].cpu().numpy().tolist()
+            max_confidence = max(scores) if scores else 0.0
+            
+            results.append({
+                "frame": frame_file,
+                "detections": len(scores),
+                "max_confidence": max_confidence,
+                "average_confidence": np.mean(scores) if scores else 0.0
+            })
+            
+        except Exception as e:
+            print(f"⚠ Error processing {frame_file}: {e}")
+            results.append({
+                "frame": frame_file,
+                "detections": 0,
+                "max_confidence": 0.0,
+                "average_confidence": 0.0
+            })
+    
+    return results
+
+# -------------------- Manipulation Detection -------------------- #
+def detect_manipulation(report_path="report.json"):
+    """Determines video authenticity based on analysis results"""
+    try:
+        with open(report_path) as f:
+            report = json.load(f)
+            
+        # Adjusted thresholds
+        CONFIDENCE_THRESHOLD = 0.80  # Reduced from 0.65
+        FLOW_STD_THRESHOLD = 28      # New standard deviation threshold
+        SUSPICIOUS_FRAME_RATIO = 0.3  # Increased from 0.25
+        
+        stats = report["summary_stats"]
+        
+        # New metrics
+        confidence_std = np.std([r["average_confidence"] for r in report["frame_analysis"]])
+        flow_std = stats.get("std_optical_flow", 0)
+        low_conf_frames = sum(1 for r in report["frame_analysis"] if r["average_confidence"] < 0.4)
+        anomaly_ratio = low_conf_frames / len(report["frame_analysis"])
+
+        # Multi-factor scoring
+        score = 0
+        if stats["average_detection_confidence"] < CONFIDENCE_THRESHOLD:
+            score += 1.5
+        if flow_std > FLOW_STD_THRESHOLD:
+            score += 1.2
+        if anomaly_ratio > SUSPICIOUS_FRAME_RATIO:
+            score += 1.0
+        if confidence_std > 0.2:  # High variance in confidence
+            score += 0.8
+
+        return score
+
+    except Exception as e:
+        return f"❌ Error in analysis: {str(e)}"
+
+# -------------------- Reporting -------------------- #
+# -------------------- Reporting -------------------- #
+def generate_report(visual_results, flow_results, output_file="report.json"):
+    """Generates comprehensive analysis report"""
+    report_data = {
+        "frame_analysis": visual_results,
+        "motion_analysis": flow_results,
+        "summary_stats": {
+            "max_detection_confidence": max(r["max_confidence"] for r in visual_results),
+            "average_detection_confidence": np.mean([r["average_confidence"] for r in visual_results]),
+            "detection_confidence_std": np.std([r["average_confidence"] for r in visual_results]),
+            "peak_optical_flow": max(r["max_flow"] for r in flow_results) if flow_results else 0,
+            "average_optical_flow": np.mean([r["mean_flow"] for r in flow_results]) if flow_results else 0,
+            "std_optical_flow": np.std([r["mean_flow"] for r in flow_results]) if flow_results else 0
+        }
+    }
+    
+    with open(output_file, "w") as f:
+        json.dump(report_data, f, indent=2)
+
+    # ... rest of visualization code ...
+    
+    return report_data  # Added return statement
+
+# -------------------- Main Pipeline -------------------- #
+def analyze_video(video_path):
+    """Complete video analysis workflow"""
+    print("\n📋 Metadata Extraction:")
+    metadata = extract_metadata(video_path)
+    print(json.dumps(metadata.get("streams", [{}])[0], indent=2))
+
+    print("\n🎞 Frame Extraction:")
+    frame_count = extract_frames(video_path)
+    print(f"✅ Extracted {frame_count} frames at {FRAME_EXTRACTION_INTERVAL}s intervals")
+
+    print("\n🔍 Running object detection...")
+    visual_results = detect_objects("frames")
+
+    print("\n🌀 Calculating optical flow...")
+    flow_results = calculate_optical_flow("frames")
+
+    print("\n📊 Generating Final Report...")
+    report_data = generate_report(visual_results, flow_results)
+
+    print("\n🔍 Authenticity Analysis:")
+    score = detect_manipulation()  # This function should return a score
+
+    print(f"\n🎯 Final Score: {score}")  # Debugging line
+    return score  # ✅ Ensure this score is returned properly
+
+
+
+# -------------------- Execution -------------------- #
+
+
+
+#--------------------------------Streamlit---------------------------------------------#
+#--------------------------------Streamlit---------------------------------------------#
+import streamlit as st
+import tempfile
+def local_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+local_css("style.css")  # Ensure you have a separate style.css file
+
+# Sidebar for Navigation
+# Navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("", ["Home", "Analyze Video", "About"])
+
+# Home Page
+if page == "Home":
+    st.markdown("<h1 class='title'>Video Manipulation Detection</h1>", unsafe_allow_html=True)
+    
+    # Hero Section
+    col1, col2 = st.columns(2)
+    with col1:
+        st.markdown("""
+        <div class='hero-text'>
+            Detect manipulated videos with AI-powered analysis.
+            Protect yourself from deepfakes and synthetic media.
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.video("Realistic Universe Intro_free.mp4")  # Add sample video URL
+    
+    # Features Section
+    st.markdown("## How It Works")
+    cols = st.columns(3)
+    with cols[0]:
+        st.image("upload-icon.png", width=100)
+        st.markdown("### Upload Video")
+    with cols[1]:
+        st.image("analyze-icon.png", width=100)
+        st.markdown("### AI Analysis")
+    with cols[2]:
+        st.image("result-icon.png", width=100)
+        st.markdown("### Get Results")
+
+
+elif page == "Analyze Video":
+    uploaded_file = st.file_uploader("Upload a Video", type=["mp4", "mov"])
+
+    if uploaded_file is not None:
+        # Save uploaded file to a temporary location
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
+            temp_file.write(uploaded_file.read())
+            temp_video_path = temp_file.name  # ✅ Correct variable name
+
+        st.video(temp_video_path)
+
+        if st.button("Analyze Video"):
+            with st.spinner("Analyzing..."):
+                try:
+                    score = analyze_video(temp_video_path)  # ✅ Ensure function exists
+                    
+                    # Debugging Line
+                    st.write(f"Analysis Score: {score}")  
+                    float(score)
+                    # Display result based on score
+                    if score >= 3.5 :
+                        st.markdown(f"""
+                        <div class='result-box suspicious'>
+                            <p>This video shows major signs of manipulation</p>
+                        </div>
+                        """, unsafe_allow_html=True)
+                    elif score >= 2.0:
+                        st.markdown(f"""
+                        <div class='result-box suspicious'>
+                            <p>This video shows minor signs of manipulation</p>
+                        </div>
+                        """, unsafe_allow_html=True)
+                    else:
+                        st.markdown(f"""
+                        <div class='result-box clean'>
+                            <p>No significant manipulation detected</p>
+                        </div>
+                        """, unsafe_allow_html=True)
+                except Exception as e:
+                    st.error(f"An error occurred during analysis: {e}")
+
+elif page == "About":  # ✅ Now this will work correctly
+    st.markdown("<h1 class='title'>About Us</h1>", unsafe_allow_html=True)
+    
+    # Creator Profile
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image("creator.jpg", width=300, caption="Ayush Agarwal, Lead Developer")
+    with col2:
+        st.markdown("""
+        <div class='about-text'>
+            ## Ayush Agarwal ,
+            Student at VIT Bhopal University ,
+            AIML enthusiast 
+            <br><br>
+            📧 [email protected]
+            <br>
+            🔗 [LinkedIn](www.linkedin.com/in/ayush20039939)
+            <br>
+            🐙 [GitHub](https://github.com)
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Technology Stack
+    st.markdown("## Our Technology")
+    st.markdown("""
+    <div class='tech-stack'>
+        <img src='https://img.icons8.com/color/96/000000/python.png'/>
+        <img src='https://img.icons8.com/color/96/000000/tensorflow.png'/>
+        <img src='https://img.icons8.com/color/96/000000/opencv.png'/>
+        <img src='https://raw.githubusercontent.com/github/explore/968d1eb8fb6b704c6be917f0000283face4f33ee/topics/streamlit/streamlit.png'/>
+    </div>
+    """, unsafe_allow_html=True)

requirements.txt

@@ -0,0 +1,10 @@
+streamlit==1.18.1
+torch==2.1.0
+opencv-python==4.8.0.74
+numpy==1.24.2
+matplotlib==3.7.0
+tqdm==4.65.1
+Pillow==9.4.0
+transformers==4.31.0
+ffmpeg-python==0.2.0
+requests==2.28.2

style.css

@@ -0,0 +1,32 @@
+/* style.css */
+.title {
+    color: #ff6f61; /* Bright coral color for visibility */
+    font-weight: bold;
+}
+
+.hero-text {
+    color: #1e90ff; /* Dodger blue color for visibility */
+    font-weight: bold;
+    font-size: 1.2em;
+}
+
+.result-box.suspicious {
+    background-color: #ffcccb; /* Light red background */
+    color: #d8000c; /* Dark red text */
+    font-weight: bold;
+}
+
+.result-box.clean {
+    background-color: #ccffcc; /* Light green background */
+    color: #007300; /* Dark green text */
+    font-weight: bold;
+}
+
+.about-text {
+    color: #5f9ea0; /* Cadet blue color */
+    font-weight: bold;
+}
+
+.tech-stack img {
+    filter: brightness(0) invert(1); /* Invert colors for icons */
+}