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Copy-move is one of the most commonly used methods of tampering with digital images. Keypoint-based detection is recognized as effective in copy-move forgery detection (CMFD). This paper proposes an efficient CMFD method via clustering SIFT keypoints and searching the similar neighborhoods to locate tampered regions. In the proposed method, the keypoints are clustered based on scale and color, grouped into several smaller clusters and matched separately, which reduce the high time complexity caused in matching caused by the high dimensionality of SIFT. In order to locate the tampered regions accurately at pixel level finally, a novel localization algorithm is designed to compare the similar neighborhoods of matching pairs by two similarity measures, and mark the tampered regions in pixels iteratively. We experimented on three different image data sets including kinds of tampering means to compare and verify the effectiveness and robustness of proposed method. The experimental results show that the proposed method is superior to existing state-of-art methods in terms of matching time complexity, detection reliability and forgery location accuracy.

In a previous paper, we have argued that what constitutes human-level performance for Atari games must include the measurements of both the score achieved as well as the time taken to learn to achieve it. When both of these measurements approach the level of humans, the method and system will also become practical as high score and short time performance for learning can lead to practical real-world applications. Other well-known approaches, such as DeepMind’s Atari game player, achieve human-level scores but the time taken to learn to play the games is many orders of magnitudes slower than that of humans, leading to inapplicability in real-world scenarios beyond the games. In our previous paper, we demonstrated the ability of a causal learning framework to learn to play the Atari game, Space Invaders, at both the score and time performance levels of a human novice. In this paper, we show that an extension of this system reported earlier leads to the ability for the system to perform at both the score and time level of a human expert. A major innovation of this paper is a “changing goal tracking” system that is not only applicable to Atari games such as Space Invaders, it is also applicable to general AI systems.

Both temporal correlation and causality between events are important for an intelligent system for prediction and problem solving purposes. However, learning temporal correlation or causality from real world data consisting of intervening noise is a challenging inductive learning problem. We propose an inductive learning method that capitalizes on a bootstrapping process to recover correlational or causal relations between events that could otherwise be obscured by noise. The method relies primarily on “explaining away” to skip over noisy events or other unrelated correlations and causalities to identify and enhance the strengths of putative correlations and causalities between events of concern. We use artificially generated event data as well as data obtained from real world videos containing events in putative correlational and causal relations to test the formula, and it is found that the formula is able to successfully uncover the putative correlations and causalities involved.