Currently, most quantization based data hiding al-gorithms are built assuming specific distributions of at-tacks, such as additive white Gaussian noise (AWGN), uniform noise, and so on. In this paper, we prove that the worst case additive attack for quantization based data hiding is a 3-δ function

This article deals with traitor tracing which is also known as active fingerprinting, content serialization, or user forensics. We study the impact of worst case attacks on the well-known Tardos binary probabilistic traitor tracing code, and especially its optimum setups recently advised by Amiri

This article deals with traitor tracing which is also known as active fingerprinting, content serialization, or user forensics. We study the impact of worst case attacks on the well-known Tardos binary probabilistic traitor tracing code, and especially its optimum setups recently advised by Amiri

model, combined with our understanding of what an attack could accomplish. Although our estimates are at best approximations, we speculate that a plausible worst-case worm could cause $50 billion or more in direct economic damage by attacking widelyused services in Microsoft Windows and carrying a

Recent work has shown the necessity of considering an attacker’s background knowledge when reasoning about privacy in data publishing. However, in practice, the data publisher does not know what background knowledge the attacker possesses. Thus, it is important to consider the worst-case

the attack and simply using an optimal estimator under normal operation or taking the worst-case attack into account and using a minimax estimator that minimizes the cost under the worst-case attack. In contrast, we seek to characterize the optimal tradeoff between the MSE under normal operation and the MSE

The main goal of this study consists in the development of the worst case additive attack (WCAA) for quantization-based methods using as design criteria the bit error rate probability and the maximum achievable rate of reliable communications. Our analysis is focused on the practical scheme known

This paper describes readily available techniques for auto-mating the search for worst-case (e.g., “hot case”, “cold case”) design scenarios using only modest computational resources. These methods not only streamline a repetitive yet crucial task, they usually produce better results. The problems

on the back-off manipulation by colluding selfish nodes. We cast the problem within a minimax robust detection framework, providing a detection rule of optimum performance for the worst-case attack. We analyze the effects of a single optimal attacker with respect to the detection delay and average number

of components, limited by cardinality or “interdiction resource, ” whose destruction maximizes economic losses to customers (and can thereby guide defensive measures). The decomposition subproblem typically incorporates a set of dc optimal power-flow models that cover various states of repair after an attack