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Passive image forgery detection has attracted many researchers in the recent years. Image manipulation becomes easier than before because of the fast development of digital image editing software. Image splicing is one of the most widespread methods for tampering images. Research on detection of image splicing still carries great challenges. In this paper, an algorithm based on deep learning approach and wavelet transform is proposed to detect the spliced image. In the deep learning approach, Convolution Neural Network (CNN) is employed to automatically extract features from the spliced image. CNN is applied and then Haar Wavelet Transform (HWT) is used. Support Vector Machine (SVM) is used later for classification. Additional experiments are performed. That is, Discrete Cosine Transform (DCT) replaces HWT and then Principle Component Analysis (PCA) is applied. The proposed algorithm is evaluated on a publicly available image splicing datasets (CASIA v1.0 and CASIA v2.0). It achieves high accuracy while using a relatively low dimension feature vector. Our results demonstrate that the proposed algorithm is effective and accomplishes better performance for detecting the spliced image.

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This paper describes a new efficient cryptosystem for the color image encryption technique, based on a combination of multidimensional proposed chaos systems. This chaos system consists of six bisections: 𝑻 𝟏 (𝒙), 𝑻 𝟐 (𝒙), 𝑻 𝟐 (𝒚), 𝑻 𝟑 (𝒙), 𝑻 𝟑 (𝒚), and 𝑻 𝟑 (𝒛) . They induce three chaotic matrix keys and three chaotic vector keys. We use a multidimensional chaotic system together with an encryption algorithm to provide better security and wide key spaces. The proposed cryptosystem uses four levels of random pixel diffusions and permutations simultaneously and 𝝎 -times interchange between rows and columns. The correlations between the RGB components of the plain image are reduced. The level of security, the computational complexity, the quality of decoding a decrypted image under closure threat is improved. The simulation results showed that the algorithm shows a high level of security, and the assurance that the image recovered at the receiving point is identified as the image at the transmission point.