Yassine Yousfi

ES 2340

Engineering & Science Building

85 Murray Hill Rd, Vestal, NY 13850

I am a PhD candidate at Binghamton University (NY). My research focus is on Machine Learning and Deep Learning and their applications in digital media security such as Steganography/Steganalysis or Watermarking. I also hold a MSc in Computer Science/Machine Learning from École Centrale de Lille (France).

News

Nov 24, 2020	Our paper: “ImageNet Pre-trained CNNs for JPEG Steganalysis” was accepted at the In IEEE International Workshop on Information Forensics and Security (WIFS).
Sep 16, 2020	I am a co-instructor of record of a new graduate level course at Binghamton University: Deep Learning for ECE! Read about it here.
Jul 1, 2020	My team placed 2nd at the Alaska2 Steganalysis challenge on Kaggle! Read about it here.
May 31, 2020	Our paper “An Intriguing Struggle of CNNs in JPEG Steganalysis and the OneHot Solution” is accepted at IEEE SPL. Read about it here.
Jan 30, 2019	We ranked first in the Alaska1 Steganalysis challenge ALASKA, Learn more here.

Selected publications

WIFS

ImageNet Pre-trained CNNs for JPEG Steganalysis

Yousfi, Y., Butora, J., Khvedchenya, E., and Fridrich, J.

In IEEE International Workshop on Information Forensics and Security 2020

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In this paper, we investigate pre-trained computervision deep architectures, such as the EfficientNet, MixNet, and ResNet for steganalysis. These models pre-trained on ImageNet can be rather quickly refined for JPEG steganalysis while offering significantly better performance than CNNs designed purposely for steganalysis, such as the SRNet, trained from scratch. We show how different architectures compare on the ALASKA II dataset. We demonstrate that avoiding pooling/stride in the first layers enables better performance, as noticed by other top competitors, which aligns with the design choices of many CNNs designed for steganalysis. We also show how pre-trained computer-vision deep architectures perform on the ALASKA I dataset.
SPL

An Intriguing Struggle of CNNs in JPEG Steganalysis and the OneHot Solution

Yousfi, Y., and Fridrich, J.

IEEE Signal Processing Letters 2020

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Deep convolutional neural networks (CNNs) have become the tool of choice for steganalysis because they outperform older feature-based detectors by a large margin. However, recent work points at cases where feature-based detectors perform better than CNNs due to their failure to compute simple statistics of DCT coefficients. We introduce a shallow “OneHot” CNN, which encodes DCT coefficients using clipped one-hot encoding into a binary volumetric representation of the DCT plane fed to a convolutional block designed to learn relevant intra-block and inter-block relationships using vanilla and dilated convolutions. Methodology for plugging the “OneHot” network into conventional steganalysis CNNs is also introduced for an end-to-end learnable detector with improved performance
EI

JPEG steganalysis detectors scalable with respect to compression quality

Yousfi, Y., and Fridrich, J.

In Proceedings IS&T, Electronic Imaging, Media Watermarking, Security, and Forensics 2020

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Practical steganalysis inevitably involves the necessity to deal with a diverse cover source. In the JPEG domain, one key element of the diversification is the JPEG quality factor, or, more generally, the JPEG quantization table used for compression. This paper investigates experimentally the scalability of various steganalysis detectors w.r.t. JPEG quality. In particular, we report that CNN detectors as well as older feature-based detectors have the capacity to contain the complexity of multiple JPEG quality factors within a single model when the quality factors are properly grouped based on their quantization tables. Detectors trained on multiple JPEG qualities show no loss of detection accuracy when compared with dedicated detectors trained for a specific JPEG quality factor. We also demonstrate that CNNs (but not so much feature-based classifiers) trained on multiple qualities can generalize to unseen custom quantization tables compared to detectors trained for specific JPEG qualities. Their ability to generalize to very different quantization tables, however, remains a challenging task. A semi-metric comparing quantization tables is introduced and used to interpret our results.
IH

Breaking ALASKA: Color Separation for Steganalysis in JPEG Domain

Yousfi, Y., Butora, J., Giboulot, Q., and Fridrich, J.

In The 7th ACM Workshop on Information Hiding and Multimedia Security 2019

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This paper describes the architecture and training of detectors developed for the ALASKA steganalysis challenge. For each quality factor in the range 60–98, several multi-class tile detectors implemented as SRNets were trained on various combinations of three input channels: luminance and two chrominance channels. To accept images of arbitrary size, the detector for each quality factor was a multi-class multi-layered perceptron trained on features extracted by the tile detectors. For quality 99 and 100, a new “reverse JPEG compatibility attack” was developed and also implemented using the SRNet via the tile detector. Throughout the paper, we explain various improvements we discovered during the course of the competition and discuss the challenges we encountered and trade offs that had to be adopted in order to build a detector capable of detecting steganographic content in a stego source of great diversity.