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1. Limits of Data Driven Steganography Detectors

   Dworetzky, E. (June 2023, ACM Information Hiding and Multimedia Security Workshop)

   While deep learning has revolutionized image steganalysis in terms of performance, little is known about how much modern data-driven detectors can still be improved. In this paper, we approach this difficult and currently wide open question by working with artificial but realistic looking images with a known statistical model that allows us to compute the detectability of modern content-adaptive algorithms with respect to the most powerful detectors. Multiple artificial image datasets are crafted with different levels of content complexity and noise power to assess their influence on the gap between both types of detectors. Experiments with SRNet as the heuristic detector indicate that independent noise contributes less to the performance gap than content of the same MSE. While this loss is rather small for smooth images, it can be quite large for textured images. A network trained on many realizations of a fixed textured scene will, however, recuperate most of the loss, suggesting that networks have the capacity to approximately learn the parameters of a cover source narrowed to a fixed scene. 

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2. On Comparing Ad Hoc Detectors with Statistical Hypothesis Tests

   Dworetzky, E. (June 2023, ACM Information Hiding and Multimedia Security WOrkshop)

   This paper addresses how to fairly compare ROCs of ad hoc (or data driven) detectors with tests derived from statistical models of digital media. We argue that the ways ROCs are typically drawn for each detector type correspond to different hypothesis testing problems with different optimality criteria, making the ROCs incomparable. To understand the problem and why it occurs, we model a source of natural images as a mixture of scene oracles and derive optimal detectors for the task of image steganalysis. Our goal is to guarantee that, when the data follows the statistical model adopted for the hypothesis test, the ROC of the optimal detector bounds the ROC of the ad hoc detector. While the results are applicable beyond the field of image steganalysis, we use this setup to point out possi- ble inconsistencies when comparing both types of detectors and explain guidelines for their proper comparison. Experiments on an artificial cover source with a known model with real stegano- graphic algorithms and deep learning detectors are used to confirm our claims. 

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3. On Comparing Ad Hoc Detectors with Statistical Hypothesis Tests

   https://doi.org/10.1145/3577163.3595095  

   Dworetzky, E.; Kaziakhmedov, E.; Fridrich, J. (June 2023, ACM Information Hiding and Multimedia Security Workshop)

   This paper addresses how to fairly compare ROCs of ad hoc (or data driven) detectors with tests derived from statistical models of digital media. We argue that the ways ROCs are typically drawn for each detector type correspond to different hypothesis testing problems with different optimality criteria, making the ROCs incomparable. To understand the problem and why it occurs, we model a source of natural images as a mixture of scene oracles and derive optimal detectors for the task of image steganalysis. Our goal is to guarantee that, when the data follows the statistical model adopted for the hypothesis test, the ROC of the optimal detector bounds the ROC of the ad hoc detector. While the results are applicable beyond the field of image steganalysis, we use this setup to point out possi- ble inconsistencies when comparing both types of detectors and explain guidelines for their proper comparison. Experiments on an artificial cover source with a known model with real stegano- graphic algorithms and deep learning detectors are used to confirm our claims. 

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4. Spatial Scene Memories Are Biased Towards a Fixed Amount of Semantic Information

   https://doi.org/10.1162/opmi_a_00088  

   Greene, Michelle R.; Trivedi, Devanshi (January 2023, Open Mind)

   Abstract Scene memory has known spatial biases. Boundary extension is a well-known bias whereby observers remember visual information beyond an image’s boundaries. While recent studies demonstrate that boundary contraction also reliably occurs based on intrinsic image properties, the specific properties that drive the effect are unknown. This study assesses the extent to which scene memory might have a fixed capacity for information. We assessed both visual and semantic information in a scene database using techniques from image processing and natural language processing, respectively. We then assessed how both types of information predicted memory errors for scene boundaries using a standard rapid serial visual presentation (RSVP) forced error paradigm. A linear regression model indicated that memories for scene boundaries were significantly predicted by semantic, but not visual, information and that this effect persisted when scene depth was considered. Boundary extension was observed for images with low semantic information, and contraction was observed for images with high semantic information. This suggests a cognitive process that normalizes the amount of semantic information held in memory. 

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5. Semi-Supervised Image Stitching from Unstructured Camera Arrays

   https://doi.org/10.3390/s23239481  

   Nghonda_Tchinda, Erman; Panoff, Maximillian Kealoha; Tchuinkou_Kwadjo, Danielle; Bobda, Christophe (December 2023, Sensors)

   Image stitching involves combining multiple images of the same scene captured from different viewpoints into a single image with an expanded field of view. While this technique has various applications in computer vision, traditional methods rely on the successive stitching of image pairs taken from multiple cameras. While this approach is effective for organized camera arrays, it can pose challenges for unstructured ones, especially when handling scene overlaps. This paper presents a deep learning-based approach for stitching images from large unstructured camera sets covering complex scenes. Our method processes images concurrently by using the SandFall algorithm to transform data from multiple cameras into a reduced fixed array, thereby minimizing data loss. A customized convolutional neural network then processes these data to produce the final image. By stitching images simultaneously, our method avoids the potential cascading errors seen in sequential pairwise stitching while offering improved time efficiency. In addition, we detail an unsupervised training method for the network utilizing metrics from Generative Adversarial Networks supplemented with supervised learning. Our testing revealed that the proposed approach operates in roughly ∼1/7th the time of many traditional methods on both CPU and GPU platforms, achieving results consistent with established methods. 

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