In this paper we examine the achievable rate region In this context successive refinement can be understood for the problem of successive refinement of information for as the problem of seeking optimal representations of the pattern recognition systems. The pattern recognition system patterns as the resources available for memory and sensory has two stages, going from coarse to fine recognition as more data representation increase and simultaneously the number resources become available for storing internal representations X of the patterns. We present an inner and an outer bound Of patterns in the environment also increases. Similar to the on the true achievable rate region. Using these results we pattern recognition system where the rate region is characterderive conditions under which a pattern recognition system is ized by an inner and outer bound [6] we present conditions successively refinable. These conditions are similar to the Markov that describe an inner and an outer bound on the rate region condition for successive refinement in the rate-distortion problem. for the successive refinement problem.We begin in Section 2 with a brief description of the I. INTRODUCTION mathematical model for the pattern recognition system and the associated inner and outer bounds on the achievable rates. The concept of successive refinement was proposed by In Section 3, we formally define the problem of successive Equitz and Cover [1] in the context of compression of an refinement for pattern recognition systems and present inner information source subject to a distortion constraint (rate-and outer bounds on the achievable rate region. distortion problem). The idea is to seek optimal descriptions of the data that can be considered as refinements of previous II. ACHIEVABLE RATES FOR PATTERN RECOGNITION optimal descriptions. This is the same concept as multiresoluIn this section we describe the mathematical model for the tion coding. Equitz and Cover showed that the rate-distortion pattern recognition system under consideration and then state problem is successively refinable if and only if the individual the inner and outer bounds on the achievable rate region. For solutions of the problem at each step can be written as a further details and proofs please refer to [5], [6]. Figure 1 Markov chain. Further progress was made by Rimoldi [2] who shows the block diagram for the pattern recognition system. characterized the achievable rate region for the two-point rate-
SUMMARY
Due to the tremendous growth of video editing software, it has become extremely simple to introduce malicious content by manipulating multimedia data. This may include modification of videos either by adding or deleting selective frames with malicious intentions. Hence, it is essential to find the forged frames of the videos by introducing efficient and reliable video forensic methods. This article presents an automatic intra‐frame video forgery detection strategy based on a hybrid optimization tuned deep‐convolutional neural network (deep‐CNN) classifier. The significance of the proposed method lies in developing the proposed raven‐finch optimization algorithm that tunes the weights of the deep‐CNN to exhibit enhanced detection accuracy. The proposed raven‐finch optimization algorithm combines raven search agents and finches search agents, possessing the benefits of both search agents. The features of the input video frames act as the input to the deep‐CNN classifier that detects forgery. The performance of the proposed raven‐finch‐based deep CNN method is analyzed in terms of the performance indices, such as accuracy, sensitivity, and specificity. It is attained to be 97.56%, 95.48%, and 96.38%, respectively, which shows the superiority of the proposed method for intra‐frame forgery detection.
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