Guorui Feng

Efficient reversible data hiding in encrypted images

This paper proposes a novel scheme of reversible data hiding in encrypted images based on lossless compression of encrypted data. In encryption phase, a stream cipher is used to mask the original content. Then, a data hider compresses a part of encrypted data in the cipher-text image using LDPC code, and inserts the compressed data as well as the additional data into the part of encrypted data itself using efficient embedding method. Since the majority of encrypted data are kept unchanged, the quality of directly decrypted image is satisfactory. A receiver with the data-hiding key can successfully extract the additional data and the compressed data. By exploiting the compressed data and the side information provided by the unchanged data, the receiver can further recover the original plaintext image without any error. Experimental result shows that the proposed scheme significantly outperforms the previous approaches.

Compressing Encrypted Images With Auxiliary Information

This paper proposes a novel scheme of compressing encrypted images with auxiliary information. The content owner encrypts the original uncompressed images and also generates some auxiliary information, which will be used for data compression and image reconstruction. Then, the channel provider who cannot access the original content may compress the encrypted data by a quantization method with optimal parameters that are derived from a part of auxiliary information and a compression ratio-distortion criteria, and transmit the compressed data, which include an encrypted sub-image, the quantized data, the quantization parameters and another part of auxiliary information. At receiver side, the principal image content can be reconstructed using the compressed encrypted data and the secret key. Experimental result shows the ratio-distortion performance of the proposed scheme is better than that of previous techniques.

Dynamic Adjustment of Hidden Node Parameters for Extreme Learning Machine

Extreme learning machine (ELM), proposed by Huang et al., was developed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. ELMs have been proved very fast and effective especially for solving function approximation problems with a predetermined network structure. However, it may contain insignificant hidden nodes. In this paper, we propose dynamic adjustment ELM (DA-ELM) that can further tune the input parameters of insignificant hidden nodes in order to reduce the residual error. It is proved in this paper that the energy error can be effectively reduced by applying recursive expectation-minimization theorem. In DA-ELM, the input parameters of insignificant hidden node are updated in the decreasing direction of the energy error in each step. The detailed theoretical foundation of DA-ELM is presented in this paper. Experimental results show that the proposed DA-ELM is more efficient than the state-of-art algorithms such as Bayesian ELM, optimally-pruned ELM, two-stage ELM, Levenberg-Marquardt, sensitivity-based linear learning method as well as the preliminary ELM.

Block cipher based separable reversible data hiding in encrypted images

While most reversible data hiding in encrypted images (RDH-EI) are based on stream cipher, this paper aims to present an alternative method feasible for block-enciphered images. Before uploading data to a remote server, the content owner encrypts the original image with a block cipher algorithm using an encryption key. Then, the server embeds additional bits into the encrypted image with an embedding key to generate the marked encrypted image. On the recipient side, the additional bits can be extracted if the receiver has the embedding key. In case the receiver has only the encryption key, the marked encrypted image can be directly deciphered to a plaintext image with good quality. When both the embedding and encryption keys are available for the receiver, he can recover the original image without any errors. Compared with the existing block cipher based RDH-EI method, drawbacks of the encryption and the recovery are avoided, and good embedding payloads are achieved.

Reversible Data Hiding in Encrypted Images Based on Progressive Recovery

This paper proposes a method of reversible data hiding in encrypted images (RDH-EI) based on progressive recovery. Three parties are involved in the framework, including the content owner, the data-hider, and the recipient. The content owner encrypts the original image using a stream cipher algorithm and uploads a ciphertext to the server. The data-hider on the server divides the encrypted image into three channels and, respectively, embeds different amount of additional bits into each one to generate a marked encrypted image. On the recipient side, additional message can be extracted from the marked encrypted image, and the original image can be recovered without any errors. While most of the traditional methods use one criterion to recover the whole image, we propose to do the recovery by a progressive mechanism. Rate-distortion of the proposed method outperforms state-of-the-art RDH-EI methods.

Unbalanced JPEG image steganalysis via multiview data match

This work focuses on the problem of unbalance JPEG images steganalysis.A semi-supervised learning algorithm which integrates weighted Fisher linear discriminant and K-means clustering (WFLDK) into a join framework is devised to solve unbalanced image steganalysis.Multiview match resampling method is proposed to rebalance the unbalanced training images. Image steganalysis must address the matter of learning from unbalanced training sets where the cover objects (normal images) always greatly outnumber the stego ones. But the research in unbalanced image steganalysis is seldom seen. This work just focuses on the problem of unbalance JPEG images steganalysis. In this paper, we propose a frame of feature dimension reduction based semi-supervised learning for high-dimensional unbalanced JPEG image steganalysis. Our method uses standard steganalysis features, and selects the confident stego images from the unlabeled examples by multiview match resampling method to rebalance the unbalanced training images. Furthermore, weighted Fisher linear discriminant (WFLD) is proposed to find the proper feature subspace where K-means provides the weight factor for WFLD in return. Finally, WFLD and K-means both work in an iterative fashion until convergence. Experimental results on the MBs and nsF5 steganographic methods show the usefulness of the developed scheme over current popular feature spaces.

Macroblock-level adaptive search range algorithm for motion estimation in multiview video coding

Multiview video coding (MVC) is an ongoing standard. In the working draft, motion estimation and disparity estimation are both employed in the encoding procedure. It achieves the highest possible coding efficiency, but results in extremely large encoding time, which obstructs it from practical applications. We propose a macroblock (MB) level adaptive search range algorithm utilizing inter-view correlation for motion estimation in MVC to reduce the complexity of the coder. For multi-view sequences, the motion vectors of the corresponding MBs in previously coded view are first extracted to analyze motion homogeneity. On the basis of motion homogeneity, MBs are classified into three types (MB in the region with homogeneous motion, with medium homogeneous motion, or with complex motion), and search range is adaptively determined for each type MB. Experimental results show that our algorithm can save 75% average computational complexity of motion estimation, with negligible loss of coding efficiency.

How to Extract Image Features based on Co-occurrence Matrix Securely and Efficiently in Cloud Computing

High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted server. In this paper, we propose a verifiable outsourcing scheme of feature extraction based on co-occurrence matrix with single untrusted cloud server. The original images are protected from the server by using a projection of one to many with trapdoor, which can be realized by a symmetric probabilistic encryption scheme we present. The analyzer can obtain true results of feature extraction and detect any failure with a probability of 1 if the server misbehaves. Finally, we provide the simulations on the outsourcing of extracting ccJRM features in cloud computing. The theory analysis and experiment result also show that the proposed outsourcing scheme could greatly decrease the computation cost of the analyzer without exposure of the original images and extraction results.

Effective feature selection for image steganalysis using extreme learning machine

Abstract. Image steganography delivers secret data by slight modifications of the cover. To detect these data, steganalysis tries to create some features to embody the discrepancy between the cover and steganographic images. Therefore, the urgent problem is how to design an effective classification architecture for given feature vectors extracted from the images. We propose an approach to automatically select effective features based on the well-known JPEG steganographic methods. This approach, referred to as extreme learning machine revisited feature selection (ELM-RFS), can tune input weights in terms of the importance of input features. This idea is derived from cross-validation learning and one-dimensional (1-D) search. While updating input weights, we seek the energy decreasing direction using the leave-one-out (LOO) selection. Furthermore, we optimize the 1-D energy function instead of directly discarding the least significant feature. Since recent Liu features can gain considerable low detection errors compared to a previous JPEG steganalysis, the experimental results demonstrate that the new approach results in less classification error than other classifiers such as SVM, Kodovsky ensemble classifier, direct ELM-LOO learning, kernel ELM, and conventional ELM in Liu features. Furthermore, ELM-RFS achieves a similar performance with a deep Boltzmann machine using less training time.

Reversible Data Hiding in Encrypted Images Based on Image Interpolation

This paper proposes a novel method for data hiding in encrypted image using image interpolation. Before the image encryption, the original image is sampled and an interpolation algorithm is used to calculate an estimation of the original image. Errors between the original image and the estimated image are compressed by Huffman encoding, which are further embedded into the estimated image to generate the redundant room. After image encryption using an encryption key, the secret bits are embedded into the reserved room. On the receiver side, the hidden bits can be extracted and the original content of the image can be perfectly recovered. Compared with the published results, the proposed method provides a larger embedding payload.