Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Overview

Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics

@WIFS2021 (Montpellier, France)

Rony Abecidan, Vincent Itier, Jeremie Boulanger, Patrick Bas

Installation

To be able to reproduce our experiments and do your own ones, please follow our Installation Instructions

Architecture used

Domain Adaptation in action

  • Source : Half of images from the Splicing category of DEFACTO
  • Target : Other half of the images from the Splicing category of DEFACTO, compressed to JPEG with a quality factor of 5%

To have a quick idea of the adaptation impact on the training phase, we selected a batch of size 512 from the target and, we represented the evolution of the final embeddings distributions from this batch during the training according to the setups SrcOnly and Update($\sigma=8$) described in the paper. The training relative to the SrcOnly setup is on the left meanwhile the one relative to Update($\sigma=8$) is on the right.

Don't hesitate to click on the gif below to see it better !

  • As you can observe, in the SrcOnly setup, the forgery detector is more and more prone to false alarms, certainly because compressing images to QF5 results in creating artifacts in the high frequencies that can be misinterpreted by the model. However, it has no real difficulty to identify correctly the forged images.

  • In parallel, in the Update setup, the forgery detector is more informed and make less false alarms during the training.

Discrepancies with the first version of our article

Several modifications have been carried out since the writing of this paper in order to :

  • Generate databases as most clean as possible
  • Make our results as most reproducible as possible
  • Reduce effectively computation time and memory space

Considering that remark, you will not exactly retrieve the results we shared in the first version of the paper with the implementation proposed here. Nevertheless, the results we got from this new implementation are comparable with the previous ones and you should obtain similar results as the ones shared in this page.

For more information about the modifications we performed and the reasons behind, click here

Main references

@inproceedings{mandelli2020training,
  title={Training {CNNs} in Presence of {JPEG} Compression: Multimedia Forensics vs Computer Vision},
  author={Mandelli, Sara and Bonettini, Nicol{\`o} and Bestagini, Paolo and Tubaro, Stefano},
  booktitle={2020 IEEE International Workshop on Information Forensics and Security (WIFS)},
  pages={1--6},
  year={2020},
  organization={IEEE}
}

@inproceedings{bayar2016,
  title={A deep learning approach to universal image manipulation detection using a new convolutional layer},
  author={Bayar, Belhassen and Stamm, Matthew C},
  booktitle={Proceedings of the 4th ACM workshop on information hiding and multimedia security (IH\&MMSec)},
  pages={5--10},
  year={2016}
}

@inproceedings{long2015learning,
  title={Learning transferable features with deep adaptation networks},
  author={Long, M. and Cao, Y. and Wang, J. and Jordan, M.},
  booktitle={International Conference on Machine Learning},
  pages={97--105},
  year={2015},
  organization={PMLR}
}


@inproceedings{DEFACTODataset, 
	author = {Ga{\"e}l Mahfoudi and Badr Tajini and Florent Retraint and Fr{\'e}d{\'e}ric Morain-Nicolier and Jean Luc Dugelay and Marc Pic},
	title={{DEFACTO:} Image and Face Manipulation Dataset},
	booktitle={27th European Signal Processing Conference (EUSIPCO 2019)},
	year={2019}
}

Citing our paper

If you wish to refer to our paper, please use the following BibTeX entry

@inproceedings{abecidan:hal-03374780,
  TITLE = {{Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics}},
  AUTHOR = {Abecidan, Rony and Itier, Vincent and Boulanger, J{\'e}r{\'e}mie and Bas, Patrick},
  URL = {https://hal.archives-ouvertes.fr/hal-03374780},
  BOOKTITLE = {{WIFS 2021 : IEEE International Workshop on Information Forensics and Security}},
  ADDRESS = {Montpellier, France},
  PUBLISHER = {{IEEE}},
  YEAR = {2021},
  MONTH = Dec,
  PDF = {https://hal.archives-ouvertes.fr/hal-03374780/file/2021_wifs.pdf},
  HAL_ID = {hal-03374780}
}
Owner
Rony Abecidan
PhD Candidate @ Centrale Lille
Rony Abecidan
202 Jan 06, 2023
Adaptation through prediction: multisensory active inference torque control

Adaptation through prediction: multisensory active inference torque control Submitted to IEEE Transactions on Cognitive and Developmental Systems Abst

Cristian Meo 1 Nov 07, 2022
Paddle implementation for "Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation" (NAACL 2021)

L1-Refinement Paddle implementation for "Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation" (NAACL 2021) 🙈 A more detailed readme is co

Lincedo Lab 4 Jun 09, 2021
The original implementation of TNDM used in the NeurIPS 2021 paper (no longer being updated)

TNDM - Targeted Neural Dynamical Modeling Note: This code is no longer being updated. The official re-implementation can be found at: https://github.c

1 Jul 21, 2022
Contrastive Learning of Structured World Models

Contrastive Learning of Structured World Models This repository contains the official PyTorch implementation of: Contrastive Learning of Structured Wo

Thomas Kipf 371 Jan 06, 2023
【Arxiv】Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

SANet Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution Dependencies numpy==1.18.5 scikit_image==0.16.2 torchvision==0.8.1 to

36 Jan 05, 2023
Official PyTorch Implementation of Rank & Sort Loss [ICCV2021]

Rank & Sort Loss for Object Detection and Instance Segmentation The official implementation of Rank & Sort Loss. Our implementation is based on mmdete

Kemal Oksuz 229 Dec 20, 2022
Official codes for the paper "Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech"

ResDAVEnet-VQ Official PyTorch implementation of Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech What is in this repo? M

Wei-Ning Hsu 21 Aug 23, 2022
Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.

MidiBERT-Piano Authors: Yi-Hui (Sophia) Chou, I-Chun (Bronwin) Chen Introduction This is the official repository for the paper, MidiBERT-Piano: Large-

137 Dec 15, 2022
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

PEPit: Performance Estimation in Python This open source Python library provides a generic way to use PEP framework in Python. Performance estimation

Baptiste 53 Nov 16, 2022
This package contains deep learning models and related scripts for RoseTTAFold

RoseTTAFold This package contains deep learning models and related scripts to run RoseTTAFold This repository is the official implementation of RoseTT

1.6k Jan 03, 2023
Heart Arrhythmia Classification

This program takes and input of an ECG in European Data Format (EDF) and outputs the classification for heartbeats into normal vs different types of arrhythmia . It uses a deep learning model for cla

4 Nov 02, 2022
Code for the paper "Offline Reinforcement Learning as One Big Sequence Modeling Problem"

Trajectory Transformer Code release for Offline Reinforcement Learning as One Big Sequence Modeling Problem. Installation All python dependencies are

Michael Janner 266 Dec 27, 2022
Awesome AI Learning with +100 AI Cheat-Sheets, Free online Books, Top Courses, Best Videos and Lectures, Papers, Tutorials, +99 Researchers, Premium Websites, +121 Datasets, Conferences, Frameworks, Tools

All about AI with Cheat-Sheets(+100 Cheat-sheets), Free Online Books, Courses, Videos and Lectures, Papers, Tutorials, Researchers, Websites, Datasets

Niraj Lunavat 1.2k Jan 01, 2023
Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling Introduction This repository is code release for OctField: Hierarchical Implicit Fun

55 Dec 08, 2022
This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize over continuous domains by Brandon Amos

Tutorial on Amortized Optimization This repository contains the source code for the paper Tutorial on amortized optimization for learning to optimize

Meta Research 144 Dec 26, 2022
This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.

Polygon-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes. Section I. Description The codes a

xinzelee 226 Jan 05, 2023
Python script that allows you to automatically setup your Growtopia server.

AutoSetup Python script that allows you to automatically setup your Growtopia server. How To Use Firstly, install all the required modules that used i

Aspire 3 Mar 06, 2022
A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

A Simple Framwork for CV Pre-training Model (SOCO, VirTex, BEiT)

Sense-GVT 14 Jul 07, 2022
Main repository for the HackBio'2021 Virtual Internship Experience for #Team-Greider ❤️

Hello 🤟 #Team-Greider The team of 20 people for HackBio'2021 Virtual Bioinformatics Internship 💝 🖨️ 👨‍💻 HackBio: https://thehackbio.com 💬 Ask us

Siddhant Sharma 7 Oct 20, 2022