SunnyHaze / NCL-IML

Pre-training-free Image Manipulation Localization through Non-Mutually Contrastive Learning (ICCV2023)

🏀Jizhe Zhou, 👨‍🎓Xiaochen Ma, 💪Xia Du, 🇦🇪Ahemd Y.Alhammadi, 🏎️Wentao Feng*

Sichuan University & Xiamen University of Technology & Mohamed Bin Zayed University for Humanities

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This is the official repo of our paper Pre-training-free Image Manipulation Localization through Non-Mutually Contrastive Learning.

📸 If you feel helpful, please cite our work and star this repo. Version #AB5J61

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>Play Tips:

1. Due to Google Cloud Disk reasons, the "Preparation" section may not be running. But it WON'T break this notebook; just ignore it and execute the rest sections in sequence.
2. The loaded CaCL-Net is the NCL model proposed in our paper. The nickname "CaCL-Net" comes from a local Macau restaurant called "CaCL", where we came up with the NCL idea.
3. The 4th "Result Display" section shows some representative results of NCL on those frequently-compared images. Scroll down the right slide in this section to view all pictures. Random selection from the pic pool will be involving soon. Stay tuned.
4. We built a small playground in the 5th "Test Samples From Web" section. Substituting the default image URLs stored in "urls=[...]" with your own ones and then re-execute this section, you will get the results of TCL on your customized input! Hope you will enjoy it, and please contact us if any exception occurs.

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Visit our code directly through colab by clicking

>A Bit More About Us:

We are the Special Interest Group on IML, led by Associate Researcher 🏀 Jizhe Zhou and Professor 👨‍🏫 Jiancheng Lv, under Sichuan University🇨🇳. Please refer to here for more information.
Also, here are some of our other works. 🀄 Feel free to cite and star them.

• 1🥇 Our latest benchmark and the first pure ViT-based IML build. IML-ViT
• 2🥇 Our implementation of the MVSS-Net (ICCV 2021 by Dong et al.) in Pytorch, with training code embedded. MVSS-NetPytorch-withTraining
• 3🥈 Our corrected CASIAv2 dataset, with ground-truth mask correctly aligned. Casia2.0-Corrected
• 4🥉 Our implementation of the Mile-stone Mantran-Net (CVPR 2019 by Wu, et al.) in Pytorch, with training code embedded. Mantra-NetPytorch-withTraining
• 5🥈 Our Manipulation Mask Generator (PRML 2023 by Yang, et al.), which crawls real-life, open-posted tampered images and auto-generates high-quality IML datasets in Python, with code embedded. Manipulation-Mask-Generator
• 6🏅 Our attempts of MAE on image manipulation localization in pytorch. Manipulation-MAE

This repo will be under consistent construction. You will be teleported to our latest work right from here. Stay tuned.

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Preparation

import shutil
shutil.rmtree("SampleData")

1. Environment Configuration

!gdown https://drive.google.com/uc?id=13G-Ay5Sx7o2jpG_AdjVA2drpFXZ0R2kJ
!unzip CaCLNet.zip
Sample_path = 'SampleData'

!gdown https://drive.google.com/uc?id=1urqD-AqGiHSB8k3ruz2HJuu-mBxWqqf0
CaCLNet_path = '20230319-010.pth'

!rm CaCLNet.zip

2. Load Sample Dataset

import os
SampleList=[]
for file in os.listdir(Sample_path):
  name = os.path.join(Sample_path, file)
  SampleList.append(name)
print(SampleList)

3. Load a Pre-trained CaCL-Net Model

import torch
CaCLNet = torch.load('/content/20230319-010.pth')

4. Result Display

import cv2
import custom_transforms as tr
from PIL import Image
from torchvision import transforms
from torchvision.utils import make_grid, save_image
from utils import decode_seg_map_sequence
import torch
from matplotlib import pyplot


composed_transforms = transforms.Compose([
    tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
    tr.ToTensor()])

for name in SampleList:
    im = cv2.imread(name)
    b, g, r = cv2.split(im)
    rgb = cv2.merge([r, g, b])
    image = Image.fromarray(rgb)
    image = image.resize((512, 512), Image.BILINEAR)

    target = 0.0  # Consistent with training process
    sample = {'image': image, 'label': target}

    tensor_in = composed_transforms(sample)['image'].unsqueeze(0)
    tensor_in = tensor_in.cuda()
    CaCLNet.eval()
    _, _, _, output = CaCLNet(tensor_in)
    grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy()),
                               3, normalize=False, range=(0, 255))
    save_image(grid_image, "mask.png")
    img = cv2.imread("mask.png")
    pyplot.figure( figsize=(15,5) )
    pyplot.subplot(131)
    pyplot.imshow( rgb )
    pyplot.subplot(132)
    pyplot.imshow( img )

5. Test Samples From Web

import requests
from io import BytesIO
from PIL import Image
import numpy as np

def test_image_from_web():

  for url in urls:
    response = requests.get(url)
    rgb = np.asarray(Image.open(BytesIO(response.content)))  # pil->numpy->bgr
    image = Image.fromarray(rgb)
    image = image.resize((512, 512), Image.BILINEAR)

    target = 0.0  # Consistent with training process
    sample = {'image': image, 'label': target}
    tensor_in = composed_transforms(sample)['image'].unsqueeze(0)
    tensor_in = tensor_in.cuda()
    CaCLNet.eval()
    _, _, _, output = CaCLNet(tensor_in)
    grid_image = make_grid(decode_seg_map_sequence(torch.max(output[:3], 1)[1].detach().cpu().numpy()),
                               3, normalize=False, range=(0, 255))
    save_image(grid_image, "mask.png")
    img = cv2.imread("mask.png")
    pyplot.figure( figsize=(15,5) )
    pyplot.subplot(131)
    pyplot.imshow( rgb )
    pyplot.subplot(132)
    pyplot.imshow( img )

• Images from Internet
• you can replace the url with our own data for testing !

urls = [
  'http://nadignewspapers.com/wp-content/uploads/2019/11/Kit-11.jpg',
  'https://www.digitalforensics.com/blog/wp-content/uploads/2016/09/digital_image_forgery_detection.jpg',
  'https://assets.hongkiat.com/uploads/amazing-photoshop-skills/topps20.jpg'
]

test_image_from_web()

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Citation

@InProceedings{Zhou_2023_ICCV,
    author    = {Zhou, Jizhe and Ma, Xiaochen and Du, Xia and Alhammadi, Ahmed Y. and Feng, Wentao},
    title     = {Pre-Training-Free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {22346-22356}
}

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