Topic Controlled Steganography via Graph-to-Text Generation

Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to.However,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in concealment.In this paper,we propose a topic-controlled steganography method which is guided by graph-to-text generation.The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation.Different from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional probability.We test our method in different aspects and compare it with other text generative steganographic methods.The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

Poisoning attacks and countermeasures in intelligent networks:Status quo and prospects

Over the past years,the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life.However,using machine learning in intelligent networks also presents potential security and privacy threats.A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model.In this survey,we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time.We emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms,and analyze the strengths and limitations of corresponding defense methods in a compact form.We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.

New cross section measurements on tungsten isotopes around 14 MeV neutrons and their excitation functions

New cross sections of the^(183)W(n,α)^(180m)Hf,^(186)W(n,d*)^(185)Ta,^(182)W(n,p)^(182)Ta,^(184)W(n,p)^(184)Ta,^(182)W(n,2n)^(181)W,^(184)W(n,α)^(181)Hf,and^(186)W(n,α)^(183)Hf reactions were measured in the neutron energy range of 13.5-14.8 MeV via the activation technique to improve the database and resolve discrepancies.Monoenergetic neutrons in this energy range were produced via the T(d,n)^(4)He reaction on a solid Ti-T target.The activities of the irradiated monitor foils and samples were measured using a well-calibrated high-resolution HPGe detector.Theoretical calculations of the excitation functions of the seven nuclear reactions mentioned above in the neutron energies from the threshold to 20 MeV were performed using the nuclear theoretical model program TALYS-1.9 to aid new evaluations of cross sections on tungsten isotopes.The experimental data obtained were analyzed and compared with that of previous experiments conducted by other researchers,and with the evaluated data available in the five major evaluated nuclear data libraries of IAEA(namely ENDF/B-VIII.0 or ENDF/B-VII.0,JEFF-3.3,JENDL-4.0u+,CENDL-3.2,and BROND-3.1 or ROSFOND-2010),and the theoretical values acquired using TALYS-1.9 nuclear-reaction modeling tools.The new cross section measurements agree with those of some recent experiments and theoretical excitation curves at the corresponding energies.The consistency of the theoretical excitation curves based on TALYS-1.9 with these experimental data is better than that of the evaluated curves available in the five major nuclear data libraries of IAEA.