Watermarking for multimedia security using complex wavelets

This paper investigates the application of complex wavelet transforms to the field of digital data hiding. Complex wavelets offer improved directional selectivity and shift invariance over their discretely sampled counterparts allowing for better adaptation of watermark distortions to the host media. Two methods of deriving visual models for the watermarking system are adapted to the complex wavelet transforms and their performances are compared. To produce improved capacity a spread transform embedding algorithm is devised, this combines the robustness of spread spectrum methods with the high capacity of quantization based methods. Using established information theoretic methods, limits of watermark capacity are derived that demonstrate the superiority of complex wavelets over discretely sampled wavelets. Finally results for the algorithm against commonly used attacks demonstrate its robustness and the improved performance offered by complex wavelet transforms

MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles

Vehicular Ad-hoc NETwork (VANET), a novel technology holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as Man-in-the-Middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate and trusted content within the network. In this paper, we propose a novel trust model, namely, Man-in-the-middle Attack Resistance trust model IN connEcted vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multi-dimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data is then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the bench-marked trust model. Simulation results show that for a network containing 35% MiTM attackers, MARINE outperforms the state of the art trust model by 15%, 18%, and 17% improvements in precision, recall and F-score, respectively.N/A

Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes.

A rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.Engineering and Physical Sciences Research Council through the CSIT 2 Project under Grant EP/N508664/1

Privacy region protection for H.264/AVC with enhanced scrambling effect and a low bitrate overhead

While video surveillance systems have become ubiquitous in our daily lives, they have introduced concerns over privacy invasion. Recent research to address these privacy issues includes a focus on privacy region protection, whereby existing video scrambling techniques are applied to specific regions of interest (ROI) in a video while the background is left unchanged. Most previous work in this area has only focussed on encrypting the sign bits of nonzero coefficients in the privacy region, which produces a relatively weak scrambling effect. In this paper, to enhance the scrambling effect for privacy protection, it is proposed to encrypt the intra prediction modes (IPM) in addition to the sign bits of nonzero coefficients (SNC) within the privacy region. A major issue with utilising encryption of IPM is that drift error is introduced outside the region of interest. Therefore, a re-encoding method, which is integrated with the encryption of IPM, is also proposed to remove drift error. Compared with a previous technique that uses encryption of IPM, the proposed re-encoding method offers savings in the bitrate overhead while completely removing the drift error. Experimental results and analysis based on H.264/AVC were carried out to verify the effectiveness of the proposed methods. In addition, a spiral binary mask mechanism is proposed that can reduce the bitrate overhead incurred by flagging the position of the privacy region. A definition of the syntax structure for the spiral binary mask is given. As a result of the proposed techniques, the privacy regions in a video sequence can be effectively protected by the enhanced scrambling effect with no drift error and a lower bitrate overhead.N/

Blind Image Watermark Detection Algorithm based on Discrete Shearlet Transform Using Statistical Decision Theory

Blind watermarking targets the challenging recovery of the watermark when the host is not available during the detection stage.This paper proposes Discrete Shearlet Transform as a new embedding domain for blind image watermarking. Our novel DST blind watermark detection system uses a nonadditive scheme based on the statistical decision theory. It first computes the probability density function (PDF) of the DST coefficients modelled as a Laplacian distribution. The resulting likelihood ratio is compared with a decision threshold calculated using Neyman-Pearson criterion to minimise the missed detection subject to a fixed false alarm probability. Our method is evaluated in terms of imperceptibility, robustness and payload against different attacks (Gaussian noise, Blurring, Cropping, Compression and Rotation) using 30 standard grayscale images covering different characteristics (smooth, more complex with a lot of edges and high detail textured regions). The proposed method shows greater windowing flexibility with more sensitive to directional and anisotropic features when compared against Discrete Wavelet and Contourlets

Digital video source identification based on green-channel photo response non-uniformity (G-PRNU)

This paper proposes a simple but yet an effective new method for the problem of digital video camera identification. It is known that after an exposure time of 0.15 seconds, the green channel is the noisiest of the three RGB colour channels [5]. Based on this observation, the digital camera pattern noise reference, which is extracted using only the green channel of the frames and is called Green-channel Photo Response Non-Uniformity (G-PRNU), is exploited as a fingerprint of the camera. The green channels are first resized to a standard frame size (512x512) using bilinear interpolation. Then the camera fingerprint is obtained by a wavelet based denoising filter described in [4] and averaged over the frames. 2-D correlation coefficient is used in the detection test. This method has been evaluated using 290 video sequences taken by four consumer digital video cameras and two mobile phones. The results show G- PRNU has potential to be a reliable technique in digital video camera identification, and gives better results than PRNU

Statistical watermark detection in the transform domain for digital images

The problem of multiplicative watermark detection in digital images can be viewed as a binary decision where the observation is the possibility that watermarked samples can be thought of as a noisy environment in which a desirable signal, called watermark, may exist. In this chapter, we investigate the optimum watermark detection from the viewpoint of decision theory. Different transform domains are considered with generalised noise models. We study the effect of the watermark strength on both the detector performance and the imperceptibility of the host image. Also, the robustness issue is addressed while considering a number of commonly used attacks

NOTRINO: a NOvel hybrid TRust management scheme for INternet-Of-vehicles

Internet-of-Vehicles (IoV) is a novel technology to ensure safe and secure transportation by enabling smart vehicles to communicate and share sensitive information with each other. However, the realization of IoV in real-life depends on several factors, including the assurance of security from attackers and propagation of authentic, accurate and trusted information within the network. Further, the dissemination of compromised information must be detected and vehicle disseminating such malicious messages must be revoked from the network. To this end, trust can be integrated within the network to detect the trustworthiness of the received information. However, most of the trust models in the literature relies on evaluating node or data at the application layer. In this study, we propose a novel hybrid trust management scheme, namely, NOTRINO, which evaluates trustworthiness on the received information in two steps. First step evaluates trust on the node itself at transport layer, while second step computes trustworthiness of the data at application layer. This mechanism enables the vehicles to efficiently model and evaluate the trustworthiness on the received information. The performance and accuracy of NOTRINO is rigorously evaluated under various realistic trust evaluation criteria (including precision, recall, F-measure and trust). Furthermore, the efficiency of NOTRINO is evaluated in presence of malicious nodes and its performance is benchmarked against three hybrid trust models. Extensive simulations indicate that NOTRINO achieve over 75% trust level as compared to benchmarked trust models where trust level falls below 60% for a network with 35% malicious nodes. Similarly, 92% precision and 87% recall are achieved simultaneously with NOTRINO for the same network, comparing to benchmark trust models where precision and recall falls below 87% and 85% respectively.N/