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1. Strategic Quantization

   https://doi.org/10.1109/ISIT54713.2023.10206722  

   Akyol, Emrah; Anand Anju (January 2023, 2023 IEEE International Symposium on Information Theory (ISIT))
2. Quantization and non-quantization for higher-dimensional Ginzburg--Landau vortices

   https://doi.org/10.15781/g5bs-0m80  

   Pigati, Alessandro; Stern, Daniel (January 2023, Ars inveniendi analytica)
3. Revisiting Perturbed Quantization

   https://doi.org/10.1145/3437880.3460396  

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

   null (Ed.)

   In this work, we revisit Perturbed Quantization steganography with modern tools available to the steganographer today, including near-optimal ternary coding and content-adaptive embedding with side-information. In PQ, side-information in the form of rounding errors is manufactured by recompressing a JPEG image with a ju- diciously selected quality factor. This side-information, however, cannotbeusedinthesamefashionasinconventionalside-informed schemes nowadays as this leads to highly detectable embedding. As a remedy, we utilize the steganographic Fisher information to allocate the payload among DCT modes. In particular, we show that the embedding should not be constrained to contributing coef- ficients only as in the original PQ but should be expanded to the so-called “contributing DCT modes.” This approach is extended to color images by slightly modifying the SI-UNIWARD algorithm. Using the best detectors currently available, it is shown that by manufacturing side information with double compression, one can embedthesameamountofinformationintothedoubly-compressed cover image with a significantly better security than applying J- UNIWARD directly in the single-compressed image. At the end of the paper, we show that double compression with the same qual- ity makes side-informed steganography extremely detectable and should be avoided. 

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4. Variational Bayesian Quantization

   Yang, Yibo; Bamler, Robert; Mandt, Stephan (July 2020, Proceedings of Machine Learning Research)

   We propose a novel algorithm for quantizing continuous latent representations in trained models. Our approach applies to deep probabilistic models, such as variational autoencoders (VAEs), and enables both data and model compression. Unlike current end-to-end neural compression methods that cater the model to a fixed quantization scheme, our algorithm separates model design and training from quantization. Consequently, our algorithm enables “plug-and-play” compression with variable rate-distortion trade-off, using a single trained model. Our algorithm can be seen as a novel extension of arithmetic coding to the continuous domain, and uses adaptive quantization accuracy based on estimates of posterior uncertainty. Our experimental results demonstrate the importance of taking into account posterior uncertainties, and show that image compression with the proposed algorithm outperforms JPEG over a wide range of bit rates using only a single standard VAE. Further experiments on Bayesian neural word embeddings demonstrate the versatility of the proposed method. 

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5. Variational Bayesian Quantization

   Yang, Y; Bamler, R; Mandt, S. (March 2020, Proceedings of the 37 th International Conference on Machine Learning)

   We propose a novel algorithm for quantizing continuous latent representations in trained models. Our approach applies to deep probabilistic models, such as variational autoencoders (VAEs), and enables both data and model compression. Unlike current end-to-end neural compression methods that cater the model to a fixed quantization scheme, our algorithm separates model design and training from quantization. Consequently, our algorithm enables “plug-and-play” compression at variable rate-distortion trade-off, using a single trained model. Our algorithm can be seen as a novel extension of arithmetic coding to the continuous domain, and uses adaptive quantization accuracy based on estimates of posterior uncertainty. Our experimental results demonstrate the importance of taking into account posterior uncertainties, and show that image compression with the proposed algorithm outperforms JPEG over a wide range of bit rates using only a single standard VAE. Further experiments on Bayesian neural word embeddings demonstrate the versatility of the proposed method. 

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