Robust watermarking based on DWT and nonnegative matrix factorization

A blind robust digital image watermarking scheme using chaotic encryption and HVS in DWT domain is presented. Firstly, the original image is transformed into some sub-band coefficients using discrete wavelet transformation (DWT), and then the binary image watermark signal is encrypted using logistic chaos map before it is embedded. Taking into account the characteristics of human visual system (HVS), the watermark is embedded into the important sub-band coefficients. Because of the multi-resolution decomposition for DWT and meaningful HVS, the proposed scheme can achieve good robustness, which is demonstrated in the following experiments.

Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain

This paper proposes a novel scheme combining Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) for image watermarking. The watermarking image is separated by two parts randomly, then they are embedded into the sub-bands obtained from the original image in DWT domain by modifying the singular value. SVD is applied twice in this scheme.The use of the threshold of Just Noticeable Distortion (JND) based on human visual model determines the embedding strength and position of the watermark, the digital adaptive watermarking algorithm in a combinational domain is realized. The experimental results show that the proposed scheme has a good invisibility and performance of robustness for common attacks, simultaneously enhances the embedding capacity and security.

In defence and military scenarios, Un manned Aerial Vehicle (UA V) is used for surveillance missions. UA V's transmit live v ideo to the base station. Temporal attacks may be carried out by the intruder during video transmission. These temporal attacks can be used to add/delete objects, individuals, etc. in the live transmission feed. This can cause the video informat ion to misrepresent facts of the UA V transmission. Hence, it is needed to identify the fake video fro m the real ones. Co mpression techniques like MPEG, H.263, etc. are popularly used to compress videos. Attacker can either add/delete frames fro m v ideos to introduce/remove objects, individuals etc. from video. In order to perform attack on the video, the attacker has to uncompress the video and perform addition/deletion of frames. Once the attack is done, the attacker needs to recompress the frames to a video. Wang and Farid et. al. (1) proposed a method based on double compression technique to detect temporal fingerprints left in the video caused due to frame addit ion/deletion. Based on double MPEG compression, here we propose a video forensic technique using machine learning techniques to detect video forgery. In order to generate a unique feature vector to identify forged video, we analysed the effect of attacks on Prediction Error Sequence (PES ) in various domains like Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT) domain etc. A new PES feature γ is defined and extracted fro m DWT domain, which is proven robust training parameter for both Support Vector Machine (SVM ) and ensemble based classifier. The t rained SVM was tested for unknown videos to find video forgery. Experimental results show that our proposed video forensic is robust and efficient in detecting video forgery without any human intervention. Further the proposed system is simp ler in design and imp lementation and also scalable for testing large nu mber of v ideos.

An image watermarking method using Discrete Wavelet Transform (DWT) and Genetic Algorithm (GA) is presented for applications like content authentication and copyright protection. This method is robust to various image attacks. For watermark detection/extraction, the cover image is not essential. Gray scale images of size 512 × 512 as cover image and binary images of size 64 × 64 as watermark are used in the simulation of the proposed method. Watermark embedding is done in the DWT domain. 3rd and 2nd level detail sub-band coefficients are selected for further processing. Selected coefficients are arranged in different blocks. The size of the block and the number blocks depends on the size of the watermark. One watermark bit is embedded in each block. Then, inverse DWT operation is performed to get the required watermarked image. This watermarked image is used for transmission and distribution purposes. In case of any dispute over the ownership, the hidden watermark is decoded to solve the problem. Threshold-based method is used for watermark extraction. Control parameters are identified and optimized based on GA for targeted performance in terms of PSNR and NCC. Performance comparison is done with the existing works and substantial improvement is witnessed.

The watermark embedding strategy is one of factors that affect the robustness of the watermark. In this paper, a new embedding strategy based on the discrete wavelet transform (DWT) is proposed. Different from the existing schemes based on the DWT which have explicitly excluded LL subband coefficients from watermark embedding, we claim that watermarks should be embedded in the LL subband first, and then in high frequency subbands according to the visual significance of the subbands. We have compared the performance of our new embedding strategy with that of the commonly used ones, confirming the superiority of the proposed embedding strategy. Based on this strategy, a robust oblivious watermarking algorithm in the DWT domain is presented. It takes advantage of error correcting coding (ECC) and 2D interleaving techniques to lower the detection error probability. By taking a carefully designed measure in watermark bit embedding, watermarked images achieve a 2dB improvement in PSNR. The experimental results show that the watermark embedded with the proposed algorithm is robust and invisible.

Image hashing is an effective technique for copy detection. This article proposes a robust color image hashing scheme with nonnegative matrix factorization (NMF) and saliency maps. The proposed hashing scheme constructs a weighted feature map via the saliency map and the matrix of color vector angles. It extracts hash from the weighted feature map by using 2-D discrete wavelet transform (DWT), NMF, and ordinal measurements. The use of 2-D DWT achieves an initial data reduction, the NMF provides a parts-based representation, and the ordinal measurements can make a short and robust sequence. These techniques jointly contribute to a robust, short, and discriminative hash. Experiments of classification and copy detection are both done. Comparative results indicate that the proposed hashing scheme outperforms the compared hashing schemes.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

With the development and mature of the Internet and wireless communication techniques the copy and distribution of the digital production becomes easier and faster. The copyright protection is an urgent problem to be resolved. The watermark is a digital code unremovably, robustly, and imperceptibly embedded in the host data and typically contains information about origin, status, and/or destination of the data. Most of present watermarking algorithms embed only one watermark, however one watermark is not sufficient under some circumstances. One of the reasons of privacy is that we cannot trace the responsibility of the pirates. It would come true by means of embedding the different exclusive watermarks belong to the issuer. As the different watermarks are needed at the different time, the multiple watermarks algorithm is required. The reports about multiple watermarks scheme are rather few. Cox et al. extend the single watermark algorithm to embed multiple orthogonal watermarks. The disadvantage is that the original image is needed at the watermark detector, and the watermark capacity is small. Stankovic et al. proposed a scheme utilizing the two-dimensional Radon–Wigner distribution. The lack is that the watermark capacity is small and we cannot judge the validity of the extracted watermark directly. Tao et al. present a multiple watermark algorithm in the DWT (Discrete Wavelet Transform) domain. The binary image is used for the watermark, and is embedded into all frequencies of DWT. The shortage is that the algorithm is not blind.

Image watermarking scheme using nonnegative matrix factorization and wavelet transform

In the current scenario, information transmission through digitize way is becoming a fast variant. But security between information transmissions is a measure concern. So digital watermarking technology provides a way for prevention of unauthorized access, distinguish information against illegal exploitations and allocations and copyright protection of the digitize data. Digital watermarking method should be robust, so it would be recoverable even if a part of this is altered. For embedding and extracting the watermark from the original image many different methods are provided. In this research paper watermarking is done by the combining the features of DWT (Discrete wavelet transformation) and Discrete Cosine Transformation (DCT). Red color component of the image is used for applying the DWT for embedding the watermark. After this, DCT is implemented on the 8×8 block of the image. Using this functionality a more secure and robust image is found

In this paper, we present a new watermarking arithmetic for color images based on discrete wavelet transform (DWT). First we transform the color space from RGB to YUV. After scrambling, the U component is transformed to the frequency spectrum by 3-level DWT. We take pseudo random sequence as the watermark, and insert it into the second and third high frequency bands. Linear correlation detector is used to detect the watermark, so the watermark can be detected without original image. We employ image scrambling technology to make the linear correlation detector in this watermarking system be optimal. The experimental results shows that the scheme has good transparency and robustness.

Transform techniques generally are more robust than spatial techniques for watermark embedding. In this research, neural networks and adaptive models are utilized to estimate watermarks in the presence of noise as well as other common image processing attacks in the discrete cosine transform (DCT) and discrete wavelet transform (DWT) domains. The proposed method can be used to semi-blindly determine the estimated watermark. In this paper, a comparative study to a previous method, LMS correlation based detection, is performed and demonstrates the efficacy of the proposed adaptive neural network watermark embedding and detection scheme under different attacks. Finally, the proposed scheme in the DCT transform domain is compared to the proposed scheme in the DWT domain.

A discrete wavelet transform (DWT) based codec with EBCOT/adaptive coding mode switching/low-band-shift (LBS) is presented in this paper. The adaptive coding mode switching is used for both intra- and inter-frame coding, respectively, to improve the rate vs. distortion (R-D) performance. EBCOT, the coding engine used in JPEG2000 is adopted in this framework because of its embedded R-D optimization capability which is most suitable for the application of a perceptual distortion measure (PDM). LBS is used for motion estimation (ME) in DWT domain, while context based MQ coding is adopted to code the motion vector (MV). The context design of MV coding is derived from H.264.

Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision. Most existing works directly apply NMF on high-dimensional image datasets for computing the effective representation of the raw images. However, in fact, the common essential information of a given class of images is hidden in their low rank parts. For obtaining an effective low-rank data representation, we in this paper propose a non-negative low-rank matrix factorization (NLMF) method for image clustering. For the purpose of improving its robustness for the data in a manifold structure, we further propose a graph regularized NLMF by incorporating the manifold structure information into our proposed objective function. Finally, we develop an efficient alternating iterative algorithm to learn the low-dimensional representation of low-rank parts of images for clustering. Alternatively, we also incorporate robust principal component analysis into our proposed scheme. Experimental results on four image datasets reveal that our proposed methods outperform four representative methods.