Duplicate from Ron0420/EfficientNetV2_Deepfakes_Image_Detector

Co-authored-by: Ron Lee <Ron0420@users.noreply.huggingface.co>

.gitattributes

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FINAL-EFFICIENTNETV2-B0.zip

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+oid sha256:2006d2344a1804c269dd6c686181ee0e28213db5c52b73abfbfacf99e0d604e2
+size 22914217

FINAL-EFFICIENTNETV2-S.zip

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README.md

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+---
+title: EfficientNetV2_Deepfakes_Image_Detector
+emoji: 🌖
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+app_file: app.py
+pinned: false
+duplicated_from: Ron0420/EfficientNetV2_Deepfakes_Image_Detector
+---
+
+# Configuration
+
+`title`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+import gradio as gr
+
+import cv2
+from mtcnn.mtcnn import MTCNN
+import tensorflow as tf
+import tensorflow_addons
+import numpy as np
+
+import os
+import zipfile
+
+local_zip = "FINAL-EFFICIENTNETV2-B0.zip"
+zip_ref = zipfile.ZipFile(local_zip, 'r')
+zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')
+zip_ref.close()
+
+model = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")
+
+detector = MTCNN()
+
+def deepfakespredict(input_img ):
+
+    labels = ['real', 'fake']
+    pred = [0, 0]
+    text =""
+    text2 =""
+    
+    face = detector.detect_faces(input_img)
+
+    if len(face) > 0:
+        x, y, width, height = face[0]['box']
+        x2, y2 = x + width, y + height
+
+        cv2.rectangle(input_img, (x, y), (x2, y2), (0, 255, 0), 2)
+
+        face_image = input_img[y:y2, x:x2]
+        face_image2 = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
+        face_image3 = cv2.resize(face_image2, (224, 224))
+        face_image4 = face_image3/255
+
+        pred = model.predict(np.expand_dims(face_image4, axis=0))[0]
+
+        if pred[1] >= 0.6:
+            text = "The image is FAKE."
+        elif pred[0] >= 0.6:
+            text = "The image is REAL."
+        else:
+            text = "The image may be REAL or FAKE."
+            
+    else:
+        text = "Face is not detected in the image."
+    
+    text2 = "REAL: " + str(np.round(pred[0]*100, 2)) + "%, FAKE: " + str(np.round(pred[1]*100, 2)) + "%"
+
+    return input_img, text, text2, {labels[i]: float(pred[i]) for i in range(2)}
+
+
+title="EfficientNetV2 Deepfakes Image Detector"
+description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. \
+            To use it, simply upload your image, or click one of the examples to load them.  \
+            This demo and model represent the Final Year Project titled \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by a CS undergraduate Lee Sheng Yeh.  \
+            The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference detail is available in \"references.txt.\" \
+            The examples are used under fair use to demo the working of the model only. If any copyright is infringed, please contact the researcher via this email: tp054565@mail.apu.edu.my.\
+            "
+            
+examples = [
+                ['Fake-1.png'],
+                ['Fake-2.png'],
+                ['Fake-3.png'],
+                ['Fake-4.png'],
+                ['Fake-5.png'],
+  
+                ['Real-1.png'],
+                ['Real-2.png'],
+                ['Real-3.png'],
+                ['Real-4.png'],
+                ['Real-5.png']
+                
+           ]
+           
+             
+gr.Interface(deepfakespredict,
+                     inputs = ["image"],
+                     outputs=[gr.outputs.Image(type="pil", label="Detected face"), 
+                         "text", 
+                         "text", 
+                         gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],                     
+                     title=title,
+                     description=description,
+                     examples = examples, 
+                     examples_per_page = 5
+                     ).launch()

deepfakes-test-images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6607487fcd81fd26924917811602e5413cfe08e30fe81be5773009412a4e8557
+size 5417905

packages.txt

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+ffmpeg 
+libsm6 
+libxext6

references.txt

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+Dataset References
+
+Celeb-DF (V2)
+
+@inproceedings{Celeb_DF_cvpr20, 
+author = {Yuezun Li and Xin Yang and Pu Sun and Honggang Qi and Siwei Lyu}, 
+title = {Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics}, 
+booktitle= {IEEE Conference on Computer Vision and Patten Recognition (CVPR)}, 
+year = {2020}} 
+
+FaceForensics++ Dataset 
+
+@inproceedings{roessler2019faceforensicspp, 
+author = {Andreas R\"ossler and Davide Cozzolino and Luisa Verdoliva and Christian Riess and Justus Thies and Matthias Nie{\ss}ner}, 
+title = {Face{F}orensics++: Learning to Detect Manipulated Facial Images}, 
+booktitle= {International Conference on Computer Vision (ICCV)}, 
+year = {2019} }
+
+@MISC{DDD_GoogleJigSaw2019, 
+AUTHOR = {Dufour, Nicholas and Gully, Andrew and Karlsson, Per and Vorbyov, Alexey Victor and Leung, Thomas and Childs, Jeremiah and Bregler, Christoph}, 
+DATE = {2019-09}, 
+TITLE = {DeepFakes Detection Dataset by Google & JigSaw}}

requirements.txt

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+tensorflow
+tensorflow-addons
+facenet_pytorch
+numpy
+opencv-python
+opencv-python-headless
+mtcnn