With recent advances in machine learning and big data, it is now possible to create synthetic images that look real. Face generation is often of particular interest, as faces can be used for various purposes. However, improper use of such content can lead to the dissemination of false information, such as fake news, and thus pose a threat to society. This work studies whether people believe the truthfulness of faces using eye tracking and self-reports, including free-form textual explanations when participants encounter real and computer-generated faces. We used three different datasets for our evaluations, and our experimental results show that while people are relatively better at identifying the truthfulness of real faces and faces generated by earlier machine learning algorithms with different gazing behaviors in viewing and rating phases, they perform less accurately when deciding the truthfulness of synthetic face images that are generated by newer algorithms. Our findings provide important insights for society and policymakers.

Abstract Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.

In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.

Point clouds are one of the most promising technologies for 3D content representation. In this paper, we describe a study on quality assessment of point clouds, degraded by octree-based compression on different levels. The test contents were displayed using Screened Poisson surface reconstruction, without including any textural information, and they were rated by subjects in a passive way, using a 2D image sequence. Subjective evaluations were performed in five independent laboratories in different countries, with the inter-laboratory correlation analysis showing no statistical differences, despite the different equipment employed. Benchmarking results reveal that the state-of-the-art point cloud objective metrics are not able to accurately predict the expected visual quality of such test contents. Moreover, the subjective scores collected from this experiment were found to be poorly correlated with subjective scores obtained from another test involving visualization of raw point clouds. These results suggest the need for further investigations on adequate point cloud representations and objective Quality assessment tools.

Medical professionals are viewing an increasing number of images and videos in their clinical routine. However, various types of distortions can affect medical imaging data, and therefore impact the viewers' experienced quality and their clinical practice. Thus it is necessary to quantify this impact and understand how the viewers, i.e., medical experts, perceive the quality of (distorted) images and videos. In this paper, we present an up-to-date review of the methodologies used in the literature for the subjective quality assessment of medical images and videos and discuss their merits and drawbacks depending on the use case.

In this work, a novel framework for automated, spatially-adaptive adjustment of active contour regularization and data fidelity parameters is proposed and applied for medical image segmentation. The proposed framework is tailored upon the isomorphism observed between these parameters and the eigenvalues of diffusion tensors. Since such eigenvalues reflect the diffusivity of edge regions, we embed this information in regularization and data fidelity parameters by means of entropy-based, spatially-adaptive `heatmaps'. The latter are able to repel an active contour from randomly directed edge regions and guide it towards structured ones. Experiments are conducted on endoscopic as well as mammographic images. The segmentation results demonstrate that the proposed framework bypasses iterations dedicated to false local minima associated with noise, artifacts and inhomogeneities, speeding up contour convergence, whereas it maintains a high segmentation quality.

In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a concept of correlation channel (CC) modeling, which expresses the correlation noise as being statistically dependent on the side information (SI). Compared with classical side-information-independent (SII) noise modeling adopted in current DVC solutions, it is theoretically proven that side-information-dependent (SID) modeling improves the Wyner-Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the SID CC parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel estimation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture that employs a competitive hash-based motion estimation technique to generate high-quality SI at the decoder. Experimental results corroborate our theoretical gains and validate the accuracy of the channel estimation algorithm. The performance assessment of the proposed architecture shows remarkable and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, i.e., large groups of pictures and highly irregular motion content.

In this paper a hardware implementation of ZUC stream cipher is presented. ZUC is a stream cipher that forms the heart of the 3GPP confidentiality algorithm 128-EEA3 and the 3GPP integrity algorithm 128-EIA3, offering reliable security services in Long Term Evolution networks (LTE). A detailed hardware implementation is presented in order to reach satisfactory performance results in LTE systems. The design was coded using VHDL language and for the hardware implementation, a XILINX Virtex-5 FPGA was used. Experimental results in terms of performance and hardware resources are presented.

Low power techniques in a FPGA implementation of the hash function called Luffa are presented in this paper. This hash function is under consideration for adoption as standard. Two major gate level techniques are introduced in order to reduce the power consumption, namely the pipeline technique (with some variants) and the use of embedded RAM blocks instead of general purpose logic elements. Power consumption reduction from 1.2 to 8.7 times is achieved by means of the proposed techniques compared with the implementation without any low power issue.

A new technique for controlling the bitrate of H.264 encoded sequences is presented. Bitrate control is achieved by dropping frames directly in the compressed domain. The dropped frames are carefully selected so as to either eliminate or cause non perceptible drift errors in the decoder. The technique suits well H.264 encoded sequences such as movies and tv news, which are transmitted over wireless networks.

In this paper we propose a compressed-domain image enhancement algorithm based on color appearance modeling. We aim at providing an efficient high-quality JPEG color image enhancement algorithm, in terms of color boosting and low computational cost. For that purpose, the high-performance recently proposed iCAM06 model has been adopted, where a wide range of color appearance phenomena are predicted in an efficient manner. In order to reduce the computational complexity, a pseudo-pixel extraction algorithm is applied to the four low-frequency DCT coefficients of each block, resulting in a scaled RGB version of the input image (directly in the DCT domain), where the iCAM06 model is then applied. To cope with the problem of inadequate information of small-sized images, as only 6.25% of the DCT coefficients are used, a 2-fold upsampling is performed at a preprocessing stage. The proposed algorithm outperforms relevant methods, as it is validated by the comparative results.

With the advent of Information Technology in the medical world, various radiological modalities produce a variety of digital medical files most often datasets and images. These files as any digital asset should be protected from unwanted modification of their contents, especially as they contain vital medical information. Thus their protection and authentication seems of great importance and this need will rise along with the future standardization of exchange of data between hospitals or between patients and doctors. Watermarking, a technique first introduced for multimedia files, provides a method for authentication and protection and has been recently applied to medical images. In this paper, we propose a novel watermarking technique where the region of non-interest (RONI) of medical Magnetic Resonance Imaging (MRI) images, is used to embed the region of interest (ROI). In this way, any tampering attempt, not only will be detected, but also the image could be self-restored, back to its previous, ldquooriginalrdquo form by extracting the ROI from the RONI.

An architecture and VLSI implementation of a new block cipher called Curupira is presented in this paper. This cipher is suitable for wireless sensors and RFID applications. Our 0.13 mum implementation requires resources of 9450 gate equivalences and is capable to encrypt a plaintext in 10 clock cycles. The cipher achieves a maximum throughput up to 2361 Mbps at 246 MHz for encrypting/decrypting. When clocked at 100 KHz a throughput of up to 960 Kbps is achieved and an average power of 0.04 mW is drawn.

A new data hiding scheme for scene change detection in H.264 streams is presented. The proposed scheme takes advantage of the different block sizes used by the H.264 encoder during the inter prediction stage in order to hide the scene change information inside the encoded sequence. This information enables real time scene change detection directly from the H.264 stream.

A new method for high capacity data hiding in H.264 streams is presented. The proposed method takes advantage of the different block sizes used by the H.264 encoder during the inter prediction stage in order to hide the desirable data. It is a blind data hiding scheme, i.e. the message can be extracted directly from the encoded stream without the need of the original host video. This fragile data hiding approach can be mainly used for content-based authentication and covert communication.

JPEG2000, the new standard for still image coding, provides a new framework and an integrated toolbox to better address increasing needs for compression. It offers a wide range of functionalities such as lossless and lossy coding, embedded lossy to lossless coding, progression by resolution and quality, high compression efficiency, error resilience and region-of-interest (ROI) coding. Comparative results have shown that JPEG2000 is indeed superior to established image compression standards. Overall, the JPEG2000 standard offers the richest set of features in a very efficient way and within a unified algorithm. The price of this is its additional complexity, but this should not be perceived as a disadvantage, as the technology evolves rapidly

The transform domain is the most commonly used space for watermarking applications. Most of the common frequency transforms have been used for this purpose. We investigate the use of the Gabor (1946) transform in traditional watermarking schemes. A casting method and detection results are thoroughly examined. It seems that traditional detection thresholds are not applicable in this transform, thus a modified approach is employed to overcome this problem. Experimental results are quite encouraging.

An efficient direct method for the computation of a length-N discrete cosine transform (DCT) given two adjacent length-(N/2) DCT coefficients, is presented. The computational complexity of the proposed method is lower than the traditional approach for lengths N>8. Savings of N memory locations and 2N data transfers are also achieved.

It is now quite common in the pel-recursive approaches for motion estimation, to find applications of the Kalman filtering technique both in time and frequency domains. In the block-based approach, very few approaches are available of this technique to refine the estimation of motion vectors resulting from fast algorithms such as the three step on a 16/spl times/16-block basis. This paper proposes an 8/spl times/8-block based motion estimation which uses the Kalman filtering technique to improve the motion estimates resulting from both the three step algorithm and the previous 16/spl times/16-block based Kalman application of Kuo et al. (1996). The state-space representation uses a first order auto-regressive model. Comparative results obtained for different classes of video sequences are presented.

The non-in-place input mapping and the subsequent bit-reversal stage, for the computation of the fast discrete cosine transform, are time and space consuming. A new fast in-place input reordering algorithm is proposed, which results in fewer data transfers by a factor of N, less storage requirements by a factor of N and a speedup of well over 30%. The actual speedup depends on the exact sequence length and on machine architecture and software.>