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Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty ('feature-based aesthetics'), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver's brain ('processing-based aesthetics'), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty.

The 3D video traitor tracing approach is an effective technique for securing 3D content. This approach involves the protection of the multimedia content and the prevention of each malicious manipulation by embedding the Collusion-Secure Fingerprinting (CSF) codes. Traitor tracing aims also at retrieving back the actors who contributed to the construction of an illegal release of a multimedia product by applying an efficient tracing scheme. Depending on the position of embedding the CSF codes, the current secure 3D video content schemes can be classified into three categories: the first one embeds the fingerprint into the 2D video, the second one into the depth maps’ content, but each of these two categories protects only one component of the 3D video which gives rise to the proposal of a third category which embeds the fingerprint in both the two components of the 3D video and protects independently and simultaneously the 2D frames and the depth maps contents. This paper proposes a novel traitor tracing technique to protect the two essential components of 3D videos by embedding the CSF in the 2D frames and the depth maps by applying a novel and robust spread spectrum watermarking scheme. Experimental assessments demonstrate the effectiveness of this proposed technique by applying it to a case study of 3D games and show promising results in terms of speed and tracking accuracy constraints.

Over the past decade, the transmission of images over digital and social networks, as well as their archiving on clouds, has increased dramatically. It has become necessary and urgent to secure them during their transmission or when archiving. In this paper, we propose a new color image encryption (CIE) scheme based on a single scroll chaotic system, using neurons with two dendrites. The originality of the proposed CIE scheme is that the main encryption step is based only on color pixel scrambling, which is traditionally used for diffusion. Based on the proposed CIE scheme we have developed three different approaches. From color pixel scrambling (CPS), to full color pixel component scrambling (FCPCS), passing through intra-color pixel component scrambling (ICPCS), we show that color pixel component scrambling can act not only as a permutation, but also as a substitution, ensuring both confusion and diffusion properties. The experimental results show that using the proposed scheme, regardless of the approach used, we can efficiently obtain encrypted color images. In particular, the results obtained with the FCPCS approach show that statistical and differential attack analyses are comparable to those obtained with current state-of-the-art methods that combine permutation and substitution steps.

3D objects have come to play an essential role in industry. They can also be very large, sometimes containing millions of vertices, and therefore it is vital that they are compressed, particularly when it comes to uploading them to the web and during real time rendering. The Draco 3D object compression system, proposed by Google, is becoming an industry standard for the compression of 3D objects structured as geometric meshes or point clouds. These 3D objects are important assets which also need to be secured. In this paper, we propose the first 3D object crypto-compression method, which integrates an encryption step into Google’s Draco compression scheme, by adding an AES encryption step during Draco’s entropy encoding step. Our proposed Draco 3D object crypto-compression scheme is format compliant, has no size expansion and does not require additional information such as an auxiliary file. After the entire decoding process (joint decryption and decompression), the reconstructed 3D object obtained is identical to the one after a standard compression by Draco. Experimental results on real 3D objects and a security analysis show our proposed method is efficient.

Automated image analysis and deep learning tools such as object detection models are being used increasingly by biologists. However, biological datasets often have constraints that are challenging for the use of deep learning. Classes are often imbalanced, similar, or too few for robust learning. In this paper we present a robust method relying on hierarchical classification to perform very small object detection. We illustrate our results on a custom dataset featuring 22 classes of arthropods used to study biodiversity. This dataset shows several constraints that are frequent when using deep learning on biological data with a high class imbalance, some classes learned on only a few training examples and a high similarity between classes. We propose to first perform detection at a super-class level, before performing a detailed classification at a class level. We compare the obtained results with our proposed method to a global detector, trained without hierarchical classification. Our method succeeds in obtaining a mAP of 75 %, while the global detector only achieves a mAP of 48 %. Moreover, our method shows high precision even on classes with the less train examples. Confusions between classes with our method are fewer and are of a lesser impact. We are able achieve a more robust object classification with the use of our proposed method. This method can also enable better control on the model’s output which can be particularly valuable when handling ecological, biological or medical data for example.

Copy-move forgery detection is a challenging task in digital image forensics. Keypoint-based detection methods have proven to be very efficient to detect copied-moved forged areas in images. Although these methods are effective, the keypoint matching phase has a high complexity, which takes a long time to detect forgeries, especially for very large images such as 4K Ultra HD images. In this paper, we propose a new keypoint-based method with a new fast feature matching algorithm, based on the generalized two nearest-neighbor (g2NN) algorithm allowing us to greatly reduce the complexity and thus the computation time. First, we extract keypoints from the input image. After ordering them, we perform a match search restricted to a window around the current keypoint. To detect the keypoints, we propose not to use a threshold, which allows low intensity keypoint matching and a very efficient detection of copy-move forgery, even in very uniform or weakly textured areas. Then, we apply a new matching algorithm, and finally we compute the cluster thanks to the DBSCAN algorithm. Our experimental results show that the method we propose can detect copied-moved areas in forged images very accurately and with a very short computation time which allows for the fast detection of forgeries on 4K images.

During the last few decades, multimedia security over the cloud has become a major issue. Public-key homomorphism is an efficient approach for data hiding in encrypted images (DHEI). An original image is encrypted using a public key and sent across a network and then processed to embed a secret message directly in the encrypted domain. During the decoding step, a private key is used to obtain a marked reconstructed image, where the secret message can be extracted. In this paper, we propose an efficient method of DHEI based on the Paillier cryptosystem. Using its homomorphic properties, pixel blocks and bits of the message are multiplied in the encrypted domain resulting in an addition in the clear domain. By applying a pre-processing step on the original image before encryption, this addition becomes a least significant bits (LSB) substitution. Experimental results show that using our proposed scheme, we obtain a high payload value (1 bpp) without expanding a lot the original image size contrary to current state-of-the-art methods. Indeed, whatever the key size, the expansion rate is always equal to 2. In addition, the original image and the marked reconstructed image are very similar.

Understanding of ecological interactions is necessary for the application of biological control. Banana is the second most produced fruit worldwide and the banana weevil (Cosmopolites sordidus) is the most important pest of banana and plantain. Its biological control remains challenging because of the robustness and cryptic behaviour of the adult and the hidden development of larval stages. Researchers therefore tend to favour conservation biological control of this pest. The commonly used methods for measuring the effects of natural enemies on the regulation of this pest focus on invertebrates and may underestimate the role of vertebrates on biological control. Using cameras, we recorded the predation of sentinel adult weevils in banana plots in La Réunion island that differed in weevil infestation levels and in animal biodiversity. To facilitate image analysis, we used background subtraction to isolate moving parts of image sequences and thus detect predators and predation events. Our cameras recorded only vertebrates as predators of adult banana weevils. The most important predator appeared to be the Asian shrew (Suncus murinus), which was responsible for 67% of the predation events. Other predators included the house mouse (Mus musculus), the oriental garden lizard (Calotes versicolor), and the guttural toad (Sclerophrys gutturalis). The exact time of predation events were determined from the images metadata. It was thus possible to identify predator foraging periods that coincided with activity of adult weevils. Our results confirm that images provide useful information for biological and ecological studies. Along with other recent studies, our results suggest that the role of vertebrates in biological control may be underestimated. Based on these results, we advocate for several management implications such as the installation of hedges, grasslands, and ponds to favour these vertebrate predators of the banana weevil, possibly also favouring other vertebrate and invertebrate natural enemies.

Nowadays, mankind is living in a cyber world [...]

Implementing telemedicine solutions has recently become a trend among various research teams at an international level. Telemedicine refers to the use of modern information and communication technologies to meet the needs of citizens, patients, healthcare professionals, and healthcare providers, as well as policy makers. Telemedicine applications are very promising and have great potential; they can play a very important role in service provision by improving access, equity and quality through connecting healthcare facilities and healthcare professionals, and diminishing geographical and physical barriers. However, the transmission and access technologies of medical information raise critical issues that urgently need to be addressed, especially those related to security. Further, medical identity theft is a growing and dangerous crime. Stolen personal information can have a negative financial impact, but stolen medical information cuts to the very core of personal privacy. Medical identity theft already costs billions of dollars each year, and altered medical information can put a person’s health at risk through misdiagnosis, delayed treatment or incorrect prescriptions. Yet, the use of hand devices to store, access, and transmit medical information is outpacing the privacy and security protection on those devices. Therefore, the authenticity of the information and related medical images is of prime concern as they form the basis of inference for diagnostic purposes. In such applications, tamper proofing and guaranteed originality of medical data/information is achieved by embedding some kind of watermark(s) which must be secure and robust against malicious attacks. Robustness and security of medical data/information against attacks is an interesting, challenging area for researchers. Potential researchers are using watermarking and cryptography to disseminate security to the medical data. Further, noted researchers are using watermarking techniques in the field of healthcare to address health data management issues, including source and data authentication, efficient image archiving and retrieval, optimizing bandwidth required to transmit the data, and highlighting diagnostically significant regions.

This study presents a new local feature matching approach that relies upon Reeb graph (RG)‐based representation as well as a simple and accurate similarity estimation. The central contribution of this work is to reinforce the topological consistency conditions of the graph‐based description. Formally, the RGs are enriched with geometry signatures based on parameterisation approaches. After RG construction, the shape is segmented into Reeb charts of controlled topology mapped to its canonical planar domain. Then, two stretching signatures, corresponding to the area and angle distortion, are determined and taken as three‐dimensional‐shape descriptor. The similarity estimation is performed in two steps. The first one consists in forming the pairs of similar Reeb charts, according to the minimal distance between their corresponding signatures. The second step is to measure the global similarity which quantifies the similitude degree between all the matched Reeb charts. Retrieval experiments conducted on four publicly available databases have shown that the proposed matching scheme yields satisfactory results. Among observations, it can be noticed that despite its rapidity, the method provides an overall retrieval efficiency gain compared to very recent state‐of‐the‐art methods.

With todays geobrowsers, the tessellations are far from being smooth due to a variety of reasons: the principal being the light difference and resolution heterogeneity. Whilst the former has been extensively dealt with in the literature through classic mosaicking techniques, the latter has got little attention. We focus on this latter aspect and present two DWT domain methods to seamlessly stitch tiles of heterogeneous resolutions. The first method is local in that each of the tiles that constitute the view, is subjected to one of the three context-based smoothing functions proposed for horizontal, vertical, and radial smoothing, depending on its localization in the tessellation. These functions are applied at the DWT subband level and followed by an inverse DWT to give a smoothened tile. In the second method, though we assume the same tessellation scenario, the view field is thought to be of a sliding window which may contain parts of the tiles from the heterogeneous tessellation. The window is refined in the DWT domain through mosaicking and smoothing followed by a global inverse DWT. Rather than the traditional sense, the mosaicking employed over here targets the heterogeneous resolution. Perceptually, this second method has shown better results than the first one. The methods have been successfully applied to practical examples of both the texture and its corresponding DEM for seamless 3D terrain visualization.