CNRS researcherCRIStAL, UMR CNRS 9189, Bâtiment ESPRIT
Avenue Henri Poincaré - 59655 Villeneuve d’Ascq, France
Office S1.33pauline.puteaux(at)cnrs.fr
About me
I am currently working as CNRS researcher in the Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL), Lille, France, working on multimedia security.
My research focuses on multimedia data processing and protection in the encrypted domain, in particular on reversible data hiding in encrypted images, correction of noisy encrypted images, recompression of crypto-compressed images and forensics in the encrypted domain.
I completed my PhD degree in computer science from the Université de Montpellier, France, in 2020, in the ICAR - Image & Interaction team of the LIRMM, Montpellier, France, under the supervision of William Puech.
Projects
Digital image security plays a significant role in all fields, especially in highly confidential areas like the military and medical worlds. With the development of cloud computing, the growth in information technology has led to serious security problems where confidentiality, authentication and integrity are constantly threatened, by illegal activities like hacking, copying or malicious use of information. The aim of encryption methods is to guarantee data privacy by fully or partially randomizing the content of original images. During the transmission or the archiving of encrypted images, it is often necessary to analyze or to process them without knowing the original content, or the secret key used during the encryption phase. Many applications exist, such as visual secret sharing (VSS), data hiding in encrypted images, research and indexing in encrypted databases or recompression of crypto-compressed images.
Reversibledata hidingin encrypted images
Image encryption, (reversible) data hiding, MSB prediction.
Correction ofnoisy encryptedimages
Image encryption, image denoising, statistical analysis, deep learning.
Recompression of JPEG crypto-compressed images
JPEG compression, selective image encryption.
Education & Career
Now
CNRS
Researcher
2020-2021
Temporary research and teaching assistant
LIRMM - Univ. Montpellier (France)
2017-2020
PhD in Computer Science
LIRMM - Univ. Montpellier (France) Supervised by William Puech "Image Analysis and Processing in the Encrypted Domain"
2015-2017
Master's Degree in Applied Mathematics
Univ. Grenoble Alpes - Ensimag (France) Spe. Cybersecurity, International cursus
Publications
Journals
Rebuttal: On the Security of Reversible Data Hiding in Encrypted Images by MSB Prediction Pauline Puteaux and William PuechIEEE Transactions on Information Forensics and Security (TIFS), 2021.pdf
CFB-then-ECB Mode-Based Image Encryption for an Efficient Correction of Noisy Encrypted Images Pauline Puteaux and William PuechIEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020.pdf
Homomorphic Encryption-Based LSB Substitution for High Capacity Data Hiding in the Encrypted Domain Pauline Puteaux, Manon Vialle and William PuechIEEE Access, 2020.pdf
A Recursive Reversible Data Hiding in Encrypted Images Method with a Very High Payload Pauline Puteaux and William PuechIEEE Transactions on Multimedia (TMM), 2020.pdf
Recompression of JPEG Crypto-Compressed Images Without a Key Vincent Itier, Pauline Puteaux and William PuechIEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2019.pdf
Distortion Function for Emoji Image Steganography Lina Shi, Zichi Wang, Zhenxing Qian, Nannan Huang, Pauline Puteaux and Xinpeng ZhangComputers Mater Continua (CMC) 59: 943-953, 2019.pdf
An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted ImagesPauline Puteaux and William PuechIEEE Transactions on Information Forensics and Security (TIFS) 13(7): 1670-1681, 2018.pdf
International conferences
Hierarchical High Capacity Data Hiding in JPEG Crypto-Compressed ImagesPauline Puteaux, Zichi Wang, Xinpeng Zhang and William Puech European Signal Processing Conference (EUSIPCO): 725-729, 2020.pdf
Localization and Correction of Corrupted Pixel Blocks in Noisy Encrypted ImagesPauline Puteaux and William Puech IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA), 2020.pdf
Image Analysis and Processing in the Encrypted DomainPauline Puteaux and William Puech IEEE International Conference on Image Processing (ICIP): 3020-3022, 2019.pdf
Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing SchemeSébastien Beugnon, Pauline Puteaux and William Puech IEEE International Conference on Image Processing (ICIP): 679-683, 2019.pdf
EPE-based Huge-Capacity Reversible Data Hiding in Encrypted ImagesPauline Puteaux and William PuechIEEE International Workshop on Information Forensics and Security (WIFS), 2018.pdf
Noisy Encrypted Image Correction based on Shannon Entropy Measurement in Pixel Blocks of Very Small SizePauline Puteaux and William PuechEuropean Signal Processing Conference (EUSIPCO), 2018.pdf
Reversible Data Hiding in Encrypted Images based on Adaptive Local Entropy AnalysisPauline Puteaux and William PuechIEEE International Conference on Image Processing Theory, Tools and Applications (IPTA): 1-6, 2017.pdf
High-Capacity Reversible Data Hiding in Encrypted Images using MSB PredictionPauline Puteaux and William PuechElectronic Imaging (EI), Media Watermarking, Security, and Forensics: 10-15, 2017.pdf
High-Capacity Data Hiding in Encrypted Images using MSB predictionPauline Puteaux, Dave Trinel and William PuechIEEE International Conference on Image Processing Theory, Tools and Applications (IPTA): 1-6, 2016.pdf
National conferences
Compression sans perte de données GNSS au format RINEX dans un contexte applicatif de véhicules autonomesArnaud Soulier, Pauline Puteaux, Frédéric Comby et William PuechCompression et Représentation des Signaux Audiovisuels (CORESA), 2020.
Protection de la vie privée par partage hybride de photos sur les réseaux sociauxSébastien Beugnon, Pauline Puteaux et William PuechCompression et Représentation des Signaux Audiovisuels (CORESA), 2018.
Analyse de la taille minimale d’un bloc de pixels pour avoir une valeur significative de l’entropie : application à la correction d’images chiffréesPauline Puteaux et William PuechCompression et Représentation des Signaux Audiovisuels (CORESA), 2017.
Scientific dissemination
Research seminary: Image analysis and processing in the encrypted domainCRIStAL, Lille (France), 2021.
Ma Thèse en 180 secondes: Regional finalistRegional Final Occitanie-Méditerranée, Montpellier (France), 2020.
Three Minute Thesis Competition: Winner IEEE International Conference on Image Processing (ICIP), Taipei International Convention Center (Taiwan), 2019.
Research seminary: High capacity data hiding in encrypted imagesHangzhou Dianzi University, School of Cyberspace (China), 2018.
Presentation: Issues and security of current biometric systemsAFSIN Meeting, Francophone Days of Digital Forensics, Montpellier (France), 2018.
Practical work: Brute-force attack of an encrypted image by content analysisPre-GdR IT Security Summer school, Cyber in Occitanie, Montpellier (France), 2018.
Presentation: Data hiding in encrypted imagesGdR ISIS and GdR IT Security Meeting, Multimedia data processing in the encrypted domain, Paris (France), 2018.
Presentation: Encrypted image correction by entropic measurementGdR ISIS Meeting, Entropies, divergences and classical and generalized information measures, Paris (France), 2017.
Reversible data hiding in encrypted images
Reversible data hiding in encrypted images
(RDHEI) is an effective technique to embed data in the encrypted
domain. An original image is encrypted with a secret key
and during or after its transmission, it is possible to embed
additional information in the encrypted image, without knowing
the encryption key or the original content of the image. During
the decoding process, the secret message can be extracted and
the original image can be reconstructed. In the last few years,
RDHEI has started to draw research interest. Indeed, with the
development of cloud computing, data privacy has become a
real issue. However, none of the existing methods allows us to
hide a large amount of information in a reversible manner.
We propose a new reversible method based on
MSB (most significant bit) prediction with a very high capacity, available in three different approaches.
In the CPE-HCRDH approach, the original image
is pre-processed to avoid all PE. The pre-processed image
is encrypted and then the data hider can blindly replace all
MSB values of the encrypted image by bits of the secret
message. In this case, payload is equal to 1 bpp and the
reconstructed image corresponds to the pre-processed image
which is very close to the original one (PSNR > 50 dB).
In the EPE-HCRDH approach, the original image is encrypted
without any modification. After encryption, information about
the location of all pixels which cannot be predicted is embedded
by MSB substitution by the content owner. Then, the data hider
can detect all bits which can be marked and replaces them by
bits of the secret message. In this case, the payload is slightly
less than 1 bpp but perfect reversibility is achieved.
In the recursive approach, all the bit-planes of an
image are processed, from the most significant one to
the least significant by combining error prediction, adaptation,
encryption and embedding, as long as it is possible. In fact,
according to our obtained results, pixels in the clear domain are
highly correlated and the most significant bit-planes can easily
be predicted. Using this property, a large part of the bits of
an image can be substituted by bits of a secret message.
An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted ImagesPauline Puteaux and William PuechIEEE Transactions on Information Forensics and Security (TIFS), 2018.
A Recursive Reversible Data Hiding in Encrypted Images Method with a Very High PayloadPauline Puteaux and William PuechIEEE Transactions on Multimedia (TMM), 2020.
Correction of noisy encrypted images
Many techniques have been presented to protect image content confidentiality. The owner of an image encrypts it
using a key and transmits the encrypted image across a network.
If the recipient is authorized to access the original content of
the image, he can reconstruct it losslessly. However, if during the
transmission the encrypted image is noised, some parts of the
image can not be deciphered. In order to blindly localize and correct
these errors, we have proposed two different approaches.
The first one is based on the local Shannon entropy measurement. As
entropy value in a block of pixels in a clear image is generally
smaller than the value in the encrypted domain, it is possible to
know if a block has been correctly decrypted or not during the
decoding phase. Best results are obtained using distance map entropy and quantization.
The second one involves deep learning and in particular, CNN. A shallow
neural network is efficient to infer if a block of pixels
is clear or encrypted. In a first step, directly
from a noisy decrypted image, it is possible to make the distinction between clear blocks and probably incorrectly
decrypted ones. During a second step,
the configurations associated to each probably incorrectly decrypted
block are analyzed in order to perform the correction.
CFB-then-ECB Mode-Based Image Encryption for an Efficient Correction of Noisy Encrypted ImagesPauline Puteaux and William PuechIEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020.
Recompression of crypto-compressed images
The rising popularity of social networks and cloud
computing has greatly increased number of JPEG compressed
image exchanges. In this context, the security of the transmission
channel and/or the cloud storage can be susceptible to privacy
leaks. Selective encryption is an efficient tool to mask image
content and to protect confidentiality while remaining formatcompliant.
However, image processing in the encrypted domain
is not a trivial task. We have presented a JPEG cryptocompression
method which allows us to recompress a JPEG
crypto-compressed image several times, without any information
about the secret key or the original image content. Indeed, each recompression can be
done directly on the JPEG bitstream by removing the last bit of
the code representation of each non-zero coefficient, adapting the
entropic code part, and slightly modifying the quantization table.
The proposed method is efficient to recompress JPEG crypto-compressed
images in terms of ratio compression. Moreover, the decryption
of the recompressed image produces an image with a very similar
visual quality when compared to the original image.
Recompression of JPEG crypto-compressed images without a keyVincent Itier, Pauline Puteaux and William PuechIEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2019.
