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1. How to Pretrain for Steganalysis

   https://doi.org/10.1145/3437880.3460395  

   Butora, Jan; Yousfi, Yassine; Fridrich, Jessica (June 2021, ACM Information Hiding and Multimedia Security Workshop)

   null (Ed.)

   In this paper, we investigate the effect of pretraining CNNs on Ima- geNet on their performance when refined for steganalysis of digital images. In many cases, it seems that just ’seeing’ a large number of images helps with the convergence of the network during the refinement no matter what the pretraining task is. To achieve the best performance, the pretraining task should be related to steganal- ysis, even if it is done on a completely mismatched cover and stego datasets. Furthermore, the pretraining does not need to be carried out for very long and can be done with limited computational re- sources. An additional advantage of the pretraining is that it is done on color images and can later be applied for steganalysis of color and grayscale images while still having on-par or better perfor- mance than detectors trained specifically for a given source. The refining process is also much faster than training the network from scratch. The most surprising part of the paper is that networks pretrained on JPEG images are a good starting point for spatial domain steganalysis as well. 

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2. Data Augmentation for JPEG Steganalysis

   Yousfi, Yassine; Itzhaki, Tomer; Fridrich, Jessica (December 2021, 13th IEEE Workshop on Information Security and Forensics)

   null (Ed.)

   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 assess their benefit in JPEG steganalysis 

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3. Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques

   https://doi.org/10.1038/s41598-023-47266-7  

   Azam, Farhat Binte; Carney, Ryan M; Kariev, Sherzod; Nallan, Krishnamoorthy; Subramanian, Muthukumaravel; Sampath, Gopalakrishnan; Kumar, Ashwani; Chellappan, Sriram (December 2023, Scientific Reports)

   Abstract The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti,Anopheles stephensi, andCulex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50,MobileNetV2,EfficientNet-B0, andConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably,ResNet50andEfficientNet-B0demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively.EfficientNet-B0demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of thisEfficientNet-B0model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well. 

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4. How to Form Bags in Batch Steganography

   https://doi.org/10.1109/WIFS61860.2024.10810688  

   Dworetzky, Eli; Fridrich, Jessica (December 2024, IEEE)

   In batch steganography, the sender spreads the secret payload among multiple cover images forming a bag. The question investigated in this paper is how many and what kind of images the sender should select for her bag. We show that by forming bags with a bias towards selecting images that are more difficult to steganalyze, the sender can either lower the probability of being detected or save on bandwidth by sending a smaller bag. These improvements can be quite substantial. Our study begins with theoretical reasoning within a suitably simplified model. The findings are confirmed on experiments with real images and modern steganographic and steganalysis techniques. 

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5. STATISTICAL MODELS FOR IMAGE STEGANOGRAPHY EXPLAINING AND REPLACING HEURISTICS

   Butor, Jan (May 2021, Binghamton University magazine)

   null (Ed.)

   The steganographic field is nowadays dominated by heuristic approaches for data hiding. While there exist a few model-based steganographic algorithms designed to minimize statistical detectability of the underlying model, many more algorithms based on costs of changing a specific pixel or a DCT coefficient have been over the last decade introduced. These costs are purely heuristic, as they are designed with feedback from detectors implemented as machine learning classifiers. For this reason, there is no apparent relation to statistical detectability, even though in practice they provide comparable security to model-based algorithms. Clearly, the security of such algorithms stands only on the assumption, that the detector used to assess the security, is the best one possible. Such assumption is of course completely unrealistic. Similarly, steganalysis is mainly implemented with empirical machine learning detectors, which use hand-crafted features computed from images or as deep learning detectors - convolutional neural networks. The biggest drawback of this approach is, that the steganalyst, even though having a very good detection power, has very little to no knowledge about what part of the image or the embedding algorithm contributes to the detection, because the detector is used as a black box. In this work, we will try to leave the heuristics behind and go towards statistical models. First, we introduce statistical models for current heuristic algorithms, which helps us understand and predict their security trends. Furthemore this allows us to improve the security of such algorithms. Next, we focus on steganalysis exploiting universal properties of JPEG images. Under certain realistic conditions, this leads to a very powerful attack against any steganography, because embedding even a very small secret message breaks the statistical model. Lastly, we show how we can improve security of JPEG compressed images through additional compression. 

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