rabi3elbeji / alaska

Breaking the Alaska steganalysis challenge

🏔 ALASKA

This repo provides state-of-the-art pre-trained models for steganalysis in the JPEG domain, trained and used to win the ALASKA steganalaysis challenge. Details about the architectures can be found in our paper.

Features

• Color seperated feature maps extraction using pretrained SRNet models
• Arbitrary size steganalysis using pretrained detectors
• Notebooks to fine-tune feature extractors and train custom detectors
• Models are shared within the Tensorflow framework, and converted to ONNX for use with other deep learning frameworks.

Please note that shared models are only for JPEG quality factor 95. Please open an issue if other quality factors are needed.

Dependecies

Python 3.5+ and dependencies listed in requirements.txt. A Python3 compatible jpeg Package is included in the tools folder.

Getting started - Downloading models

Please run the following python code to download the available models.

import requests
import zipfile
import os
home = os.path.expanduser("~")
user = home.split('/')[-1]

url = 'http://dde.binghamton.edu/download/alaska/models.zip'
local = home + '/alaska/models.zip'

r = requests.get(url)
with open(local, 'wb') as f:
    for chunk in tqdm(r.iter_content(chunk_size=2**10)): 
        if chunk:
            f.write(chunk)
with zipfile.ZipFile(local, 'r') as zipref:
    zipref.extractall(home + '/alaska/')
    
os.remove(local)

Getting started - Downloading datasets

This repo comes with minimal image examples, the complete datasets are available here (note that you need to create a free account)

References

Please consider citing our paper if you find this repository useful.

@inproceedings{Yousfi2019Alaska,
 author = {Yousfi, Yassine and Butora, Jan and Fridrich, Jessica and Giboulot, Quentin},
 title = {Breaking ALASKA: Color Separation for Steganalysis in JPEG Domain},
 booktitle = {Proceedings of the ACM Workshop on Information Hiding and Multimedia Security},
 series = {IH\&\#38;MMSec'19},
 year = {2019},
 isbn = {978-1-4503-6821-6},
 location = {Paris, France},
 pages = {138--149},
 numpages = {12},
 url = {http://doi.acm.org/10.1145/3335203.3335727},
 doi = {10.1145/3335203.3335727},
 acmid = {3335727},
 publisher = {ACM},
 address = {New York, NY, USA},
}