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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() |