Yassine Yousfi

Selected publications

1. WIFS21

   Data Augmentation for JPEG Steganalysis

   Itzhaki, T., Yousfi, Y., and Fridrich, J.

   In IEEE International Workshop on Information Forensics and Security 2021

   Abs PDF Slides

   Deep Convolutional Neural Networks (CNNs) have performed remarkably well in JPEG steganalysis. However, they heavily rely on large datasets to avoid overfitting. Data augmentation is a popular technique to inflate the datasets available without collecting new images. For JPEG steganalysis, the augmentations predominantly used by researchers are limited to rotations and flips (D4 augmentations). This is due to the fact that the stego signal is erased by most augmentations used in computer vision. In this paper, we systematically survey a large number of other augmentation techniques and asses their benefit in JPEG steganalysis.

2. WIFS21

   CNN Steganalyzers Leverage Local Embedding Artifacts

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

   In IEEE International Workshop on Information Forensics and Security 2021

   Abs PDF Slides

   While convolutional neural networks have firmly established themselves as the superior steganography detectors, little human-interpretable feedback to the steganographer as to how the network reaches its decision has so far been obtained from trained models. The folklore has it that, unlike rich models, which rely on global statistics, CNNs can leverage spatially localized signals. In this paper, we adapt existing attribution tools, such as Integrated Gradients and Last Activation Maps, to show that CNNs can indeed find overwhelming evidence for steganography from a few highly localized embedding artifacts. We look at the nature of these artifacts via case studies of both modern content-adaptive and older steganographic algorithms. The main culprit is linked to “content creating changes” when the magnitude of a DCT coefficient is increased (Jsteg, –F5), which can be especially detectable for high frequency DCT modes that were originally zeros (J-MiPOD). In contrast, J-UNIWARD introduces the smallest number of locally detectable embedding artifacts among all tested algorithms.

3. IH

   Improving EfficientNet for JPEG Steganalysis

   Yousfi, Y., Butora, J., Fridrich, J., and Fuji-Tsang, C.

   In The 9th ACM Workshop on Information Hiding and Multimedia Security 2021

   Abs PDF

   In this paper, we study the EfficientNet family pre-trained on ImageNet when used for steganalysis using transfer learning. We show that certain “surgical modifications” aimed at maintaining the input resolution in EfficientNet architectures significantly boost their performance in JPEG steganalysis, establishing thus new benchmarks. The modified models are evaluated by their detection accuracy, the number of parameters, the memory consumption, and the total floating point operations (FLOPs) on the ALASKA II dataset. We also show that, surprisingly, EfficientNets in their “vanilla form” do not perform as well as the SRNet in BOSSbase+BOWS2. This is because, unlike ALASKA II images, BOSSbase+BOWS2 contains aggressively subsampled images with more complex content. The surgical modifications in EfficientNet remedy this underperformance as well.

4. WIFS20

   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

   Abs PDF Code Video

   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.

5. SPL

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

   Yousfi, Y., and Fridrich, J.

   IEEE Signal Processing Letters 2020

   Abs PDF Code

   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

6. 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

   Abs PDF

   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.

7. 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

   Abs PDF

   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.