SSGAN: Secure Steganography Based on Generative Adversarial Networks

In this paper, a novel strategy of Secure Steganograpy based on Generative Adversarial Networks is proposed to generate suitable and secure covers for steganography. The proposed architecture has one generative network, and two discriminative networks. The generative network mainly evaluates the visual quality of the generated images for steganography, and the discriminative networks are utilized to assess their suitableness for information hiding. Different from the existing work which adopts Deep Convolutional Generative Adversarial Networks, we utilize another form of generative adversarial networks. By using this new form of generative adversarial networks, significant improvements are made on the convergence speed, the training stability and the image quality. Furthermore, a sophisticated steganalysis network is reconstructed for the discriminative network, and the network can better evaluate the performance of the generated images. Numerous experiments are conducted on the publicly available datasets to demonstrate the effectiveness and robustness of the proposed method

Ensuring message embedding in wet paper steganography

International audienceSyndrome coding has been proposed by Crandall in 1998 as a method to stealthily embed a message in a cover-medium through the use of bounded decoding. In 2005, Fridrich et al. introduced wet paper codes to improve the undetectability of the embedding by nabling the sender to lock some components of the cover-data, according to the nature of the cover-medium and the message. Unfortunately, almost all existing methods solving the bounded decoding syndrome problem with or without locked components have a non-zero probability to fail. In this paper, we introduce a randomized syndrome coding, which guarantees the embedding success with probability one. We analyze the parameters of this new scheme in the case of perfect codes

Direct asymmetric reduction of levulinic acid to gamma-valerolactone: synthesis of a chiral platform molecule

Levulinic acid was directly converted to optically active (S)-gamma-valerolactone, a proposed biomass-based chiral platform molecule. By using a SEGPHOS ligand-modified ruthenium catalyst in methanol as a co-solvent, eventually, 100% chemoselectivity, and 82% enantioselectivity were achieved. The effect of the catalyst composition and reaction parameters on the activity and selectivity was investigated in detail. The conversion of a “real” biomass derived levulinic acid to optically active GVL without decreasing the enantioselectivity was also demonstrated

Correlation of Atrial Natriuretic Peptide and Cyclic Guanosine Monophosphate Plasma Concentrations in Patients with Heart Disorders During Rest and Exercise

Peer Reviewe

Conditional Adversarial Camera Model Anonymization

The model of camera that was used to capture a particular photographic image (model attribution) is typically inferred from high-frequency model-specific artifacts present within the image. Model anonymization is the process of transforming these artifacts such that the apparent capture model is changed. We propose a conditional adversarial approach for learning such transformations. In contrast to previous works, we cast model anonymization as the process of transforming both high and low spatial frequency information. We augment the objective with the loss from a pre-trained dual-stream model attribution classifier, which constrains the generative network to transform the full range of artifacts. Quantitative comparisons demonstrate the efficacy of our framework in a restrictive non-interactive black-box setting.Comment: ECCV 2020 - Advances in Image Manipulation workshop (AIM 2020

Unravelling the developmental and functional significance of an ancient Argonaute duplication

MicroRNAs (miRNAs) base-pair to messenger RNA targets and guide Argonaute proteins to mediate their silencing. This target regulation is considered crucial for animal physiology and development. However, this notion is based exclusively on studies in bilaterians, which comprise almost all lab model animals. To fill this phylogenetic gap, we characterize the functions of two Argonaute paralogs in the sea anemone Nematostella vectensis of the phylum Cnidaria, which is separated from bilaterians by ~600 million years. Using genetic manipulations, Argonaute-immunoprecipitations and high-throughput sequencing, we provide experimental evidence for the developmental importance of miRNAs in a non-bilaterian animal. Additionally, we uncover unexpected differential distribution of distinct miRNAs between the two Argonautes and the ability of one of them to load additional types of small RNAs. This enables us to postulate a novel model for evolution of miRNA precursors in sea anemones and their relatives, revealing alternative trajectories for metazoan miRNA evolution

A novel semi-fragile forensic watermarking scheme for remote sensing images

Peer-reviewedA semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote sensing images applying a tree structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of themmultispectral or hyperspectral image is used to embed the mark in it order to detect any significant modification of the original image. The image is segmented into threedimensional blocks and a tree structured vector quantizer is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image.Se presenta un esquema de marcas de agua semi-frágiles para múltiples imágenes de banda. Proponemos incorporar una marca en imágenes de detección remota, aplicando un enfoque de cuantización del vector de árbol estructurado con las definiciones de píxel, en lugar de procesar cada banda por separado. La firma de la imagen hiperespectral se utiliza para insertar la marca en el mismo orden para detectar cualquier modificación significativa de la imagen original. La imagen es segmentada en bloques tridimensionales y un cuantificador de vector de estructura de árbol se construye para cada bloque. Estos árboles son manipulados utilizando un algoritmo iteractivo hasta que el bloque resultante satisface un criterio necesario que establece la marca incrustada. El método se muestra para poder preservar la marca bajo compresión con pérdida (por encima de un umbral establecido) pero, al mismo tiempo, detecta posiblemente bloques forjados y su posición en la imagen entera.Es presenta un esquema de marques d'aigua semi-fràgils per a múltiples imatges de banda. Proposem incorporar una marca en imatges de detecció remota, aplicant un enfocament de quantització del vector d'arbre estructurat amb les definicions de píxel, en lloc de processar cada banda per separat. La signatura de la imatge hiperespectral s'utilitza per inserir la marca en el mateix ordre per detectar qualsevol modificació significativa de la imatge original. La imatge és segmentada en blocs tridimensionals i un quantificador de vector d'estructura d'arbre es construeix per a cada bloc. Aquests arbres són manipulats utilitzant un algoritme iteractiu fins que el bloc resultant satisfà un criteri necessari que estableix la marca incrustada. El mètode es mostra per poder preservar la marca sota compressió amb pèrdua (per sobre d'un llindar establert) però, al mateix temps, detecta possiblement blocs forjats i la seva posició en la imatge sencera