DL models are used to solve many complex engineering tasks, including computer vision, speech recognition, natural language processing, or stock market analysis. Building representative and highly accurate DL models is a very costly task that involves devoting significant computational resources to process large amounts of proprietary training data, which is also hard to collect. Hence, the owners of DL models seek compensation for the incurred costs from commercial exploitation of such models, but this makes DL models attractive for attackers who try to steal them and use them in an illegal manner. KeyNet is a novel watermarking framework that satisfies the main requirements for an effective and robust watermarking of DL models. In the proposed framework, any sample in a watermark carrier set can take more than one label based on where the owner signs it, where the signature is the hashed value of the owner's information, and her model. Then, multitask learning (MTL) is used to learn the original classification task and the watermarking task together. Furthermore, a private model is added to the original one to act as a private key. Both models are trained together to embed the watermark while preserving the accuracy of the original classification task. To extract a watermark from a model, the prediction of the marked model on a signed sample is passed to the private model which can provide the position of the signature. The proposed KeyNet framework is then tested on several data sets to show its effectiveness and performance. The obtained results illustrate that KeyNet preserves the utility of the DL model while embedding a robust watermark. The watermarking application is carried out in an innovative way that makes it possible to embed a large amount of information, to establish a strong link between the owner and her marked model, to prevent attackers from overwriting the watermark without losing accuracy in the original task, and to uniquely fingerprint several copies of a pre-trained DL model for a large number of users in the system. Then, four other papers present different advances in steganography and steganalysis: a high-capacity block-based steganographic scheme for images [9], an evaluation of the false-negative rate of the Stegdetect tool [10], a steganographic scheme for the MIDI format [11], and a reversible and plausible deniable covert channel for one-time passwords (OTP) based on hash chains [12]. [PERSON] et al. [9] propose an efficient and high-capacity block-based steganographic method using optimal selection for palette images. The main goal of steganography is to provide a large steganographic payload with statistically undetectable methods. A typical approach to reduce statistical detectability in steganography is to increase the embedding efficiency, defined as the number of bits that can be hidden per embedding change. Methods with higher embedding efficiency are less detectable since, under the same capacity, they change less pixel values compared to lower efficiency methods. Block-based steganography methods are designed in order to improve the embedding efficiency under a limited capacity, but they often skip the blocks with larger distortion and require a location map to record the blocks that carry embedded data. The method proposed in this paper applies block-based steganography for palette images and does not require a location map. The proposed scheme embeds several bits in a block with, at most, a pixel change, which minimizes the embedding noise. This makes it possible to embed data in all the blocks of the image, leading to a larger capacity compared with other schemes known from the literature. Experimental results comparing the proposed scheme with other works evidence an improvement in the trade-off between embedding efficiency and capacity. In Ref. [10], [PERSON] et al. analyze the false negative rates of Stegdetect, a modern automated steganalysis tool.