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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 work of \"Achieving Face Swapped Deepfakes Detection Using EfficientNetV2\" by Lee Sheng Yeh. \
The examples were extracted from Celeb-DF(V2)(Li et al, 2020) and FaceForensics++(Rossler et al., 2019). Full reference details 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, the researcher will immediately take down the examples used.\
"
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() |