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 work of \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by Lee Sheng Yeh. \ The samples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference details is available in \"references.txt.\" \ " 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()