A tool to support the creation of datasets of tampered videos

Digital Video Forensics is getting a growing interest from the Multimedia research community, as the need for methods to validate the authenticity of a video content is increasing with the number of videos freely available to the digital users. Unlike Digital Image Forensics, to our knowledge, there are not standard datasets to test video forgery detection techniques. In this paper we present a new tool to support the users in creating datasets of tampered videos. We furthermore present our own dataset and we discuss some remarks about how to create forgeries difficult to be detected by an observer, to the naked eye

The zinc finger transcription factor PLAGL2 enhances stem cell fate and activates expression of ASCL2 in intestinal epithelial cells

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IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Computer Aided Detection (CAD) systems are very important tools which help radiologists as a second reader in detecting early breast cancer in an efficient way, specially on screening mammograms. One of the challenging problems is the detection of masses, which are powerful signs of cancer, because of their poor apperance on mammograms. This paper investigates an automatic CAD for detection of breast masses in screening mammograms based on fuzzy segmentation and a bio-inspired method for pattern recognition: Artificial Immune Recognition System. The proposed approach is applied to real clinical images from the full field digital mammographic database: Inbreast. In order to validate our proposition, we propose the Receiver Operating Characteristic Curve as an analyzer of our IMCAD classifier system, which achieves a good area under curve, with a sensitivity of 100% and a specificity of 95%. The recognition system based on artificial immunity has shown its efficiency on recognizing masses from a very restricted set of training regions

Whole genome sequencing for mutation discovery in a single case of lysosomal storage disease (MPS type 1) in the dog.

Mucopolysaccharidosis (MPS) is a metabolic storage disorder caused by the deficiency of any lysosomal enzyme required for the breakdown of glycosaminoglycans. A 15-month-old Boston Terrier presented with clinical signs consistent with lysosomal storage disease including corneal opacities, multifocal central nervous system disease and progressively worsening clinical course. Diagnosis was confirmed at necropsy based on histopathologic evaluation of multiple organs demonstrating accumulation of mucopolysaccharides. Whole genome sequencing was used to uncover a frame-shift insertion affecting the alpha-L-iduronidase (IDUA) gene (c.19_20insCGGCCCCC), a mutation confirmed in another Boston Terrier presented 2 years later with a similar clinical picture. Both dogs were homozygous for the IDUA mutation and shared coat colors not recognized as normal for the breed by the American Kennel Club. In contrast, the mutation was not detected in 120 unrelated Boston Terriers as well as 202 dogs from other breeds. Recent inbreeding to select for recessive and unusual coat colors may have concentrated this relatively rare allele in the breed. The identification of the variant enables ante-mortem diagnosis of similar cases and selective breeding to avoid the spread of this disease in the breed. Boston Terriers carrying this variant represent a promising model for MPS I with neurological abnormalities in humans

Refining the PoinTER “human firewall” pentesting framework

PurposePenetration tests have become a valuable tool in the cyber security defence strategy, in terms of detecting vulnerabilities. Although penetration testing has traditionally focused on technical aspects, the field has started to realise the importance of the human in the organisation, and the need to ensure that humans are resistant to cyber-attacks. To achieve this, some organisations “pentest” their employees, testing their resilience and ability to detect and repel human-targeted attacks. In a previous paper we reported on PoinTER (Prepare TEst Remediate), a human pentesting framework, tailored to the needs of SMEs. In this paper, we propose improvements to refine our framework. The improvements are based on a derived set of ethical principles that have been subjected to ethical scrutiny.MethodologyWe conducted a systematic literature review of academic research, a review of actual hacker techniques, industry recommendations and official body advice related to social engineering techniques. To meet our requirements to have an ethical human pentesting framework, we compiled a list of ethical principles from the research literature which we used to filter out techniques deemed unethical.FindingsDrawing on social engineering techniques from academic research, reported by the hacker community, industry recommendations and official body advice and subjecting each technique to ethical inspection, using a comprehensive list of ethical principles, we propose the refined GDPR compliant and privacy respecting PoinTER Framework. The list of ethical principles, we suggest, could also inform ethical technical pentests.OriginalityPrevious work has considered penetration testing humans, but few have produced a comprehensive framework such as PoinTER. PoinTER has been rigorously derived from multiple sources and ethically scrutinised through inspection, using a comprehensive list of ethical principles derived from the research literature

Evaluating the Key Finding of Texture Smoothing & Filtering TechniquesÂ

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