Deep steganography utilizes the powerful capabilities of deep neural networks to embed and extract messages, but its reliance on an additional message extractor limits its practical use due to the added suspicion it can raise from steganalyzers. To address this problem, we propose StegaINR, which utilizes Implicit Neural Representation (INR) to implement steganography. StegaINR embeds a secret function into a stego function, which serves as both the message extractor and the stego medium for secure transmission on a public channel. Recipients only need to use a shared key to recover the secret function from the stego function, allowing them to obtain the secret message. Our approach employs continuous functions, enabling it to handle various types of messages. To our knowledge, this is the first work to introduce INR into steganography. We perform evaluations on image, climate data, and Neural Radiance Field (NeRF) synthetic dataset to test our method in different deployment contexts.

Cloud computing is an attractive platform which offers on-demand resources as services. When many cloud services are available, some may have similar or same functionalities. So cloud service recommendation, which can help users to select the services based on their preferences, become an important technique for cloud services. In this paper, we review the relevant technologies that can perform cloud service recommendation. First, we introduce the web service selection and recommendation technologies. Next, we describe QoS measurement for cloud service and different recommendation methods focusing on Collaborative filtering. Third, we discuss the research challenges and opportunities for cloud service recommendation.

The design of modern computer hardware heavily relies on third-party intellectual property (IP) cores, which may contain malicious hardware Trojans that could be exploited by an adversary to leak secret information or take control of the system. Existing hardware Trojan detection methods either require a golden reference design for comparison or extensive functional testing to identify suspicious signals. In this paper, we propose a new formal verification method to verify the security of hardware designs. The proposed solution formalizes fine grained gate level information flow model for proving security properties of hardware designs in the Coq theorem prover environment. Compare with existing register transfer level (RTL) information flow security models, our model only needs to translate a small number of logic primitives to their formal representations without the need of supporting the rich RTL HDL semantics or dealing with complex conditional branch or loop structures. As a result, a gate level information flow model can be created at much lower complexity while achieving significantly higher precision in modeling the security behavior of hardware designs. We use the AES-T1700 benchmark from Trust-HUB to demonstrate the effectiveness of our solution. Experimental results show that our method can detect and pinpoint the Trojan.

Retweet, as a main way to spread information in twitter, has been researched in a number of works. Recently research focuses on analyzing the factors of retweet behavior. However, the prediction on retweet behavior is a new challenge which is not well studied in the past. A basic fact is that different people are interested in different kinds of tweets, and they will retweet tweets which they are interested in. First, we collect tweets of different categories from valid account of famous news media as learning corpus. Second, in order to discover user interests, we classify user tweets into different categories by Bayes model. Finally, we measure user interests on tweets of different categories, and predict retweet behavior by interest measurement. This paper extends the previous study on retweet behavior, and we predict user retweet behavior as well as infer user interests. Experiment shows Bayes model has good performance on classifying tweets, and our algorithm achieves more precision than others.