Automatic detection on CMPs and the journey into the patterns of darkness

Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Are all the frames equally important?

In this work, we address the problem of measuring and predicting temporal video saliency - a metric which defines the importance of a video frame for human attention. Unlike the conventional spatial saliency which defines the location of the salient regions within a frame (as it is done for still images), temporal saliency considers importance of a frame as a whole and may not exist apart from context. The proposed interface is an interactive cursor-based algorithm for collecting experimental data about temporal saliency. We collect the first human responses and perform their analysis. As a result, we show that qualitatively, the produced scores have very explicit meaning of the semantic changes in a frame, while quantitatively being highly correlated between all the observers. Apart from that, we show that the proposed tool can simultaneously collect fixations similar to the ones produced by eye-tracker in a more affordable way. Further, this approach may be used for creation of first temporal saliency datasets which will allow training computational predictive algorithms. The proposed interface does not rely on any special equipment, which allows to run it remotely and cover a wide audience.Comment: CHI'20 Late Breaking Work

THE INFLUENCE OF CHROMATIC ABERRATION ON DEMOSAICKING

International audienceThe wide deployment of colour imaging devices owes much to the use of colour filter array (CFA). A CFA produces a mosaic image, and normally a subsequent CFA demosaick-ing algorithm interpolates the mosaic image and estimates the full-resolution colour image. Among various types of optical aberrations from which a mosaic image may suffer, chromatic aberration (CA) influences the spatial and spectral correlation through the artefacts such as blur and mis-registration, which demosaicking also relies on. In this paper we propose a simulation framework aimed at an investigation of the influence of CA on demosaicking. Results show that CA benefits de-mosaicking to some extent, however CA lowers the quality of resulting images by any means

Automatic Detection of Manipulative Consent Management Platforms and the Journey into the Patterns of Darkness

We study how to automatically classify different types of manipulative interface design pattern for Content Management Platforms (CMPs), also known as a cookie consents. Our approach uses a scraper to extract different features of CMPs. We then classify the CMP, based on these features, into one of five patterns defined specifically for CMPs. We evaluate our automatic "detector" using four different statistical measures. We also consider factors that cause misclassifications and discuss how to potentially avoid them.publishedVersio

This changes to that: combining causal and non-causal explanations to generate disease progression in capsule endoscopy.

Due to the unequivocal need for understanding the decision processes of deep learning networks, both modal-dependent and model-agnostic techniques have become very popular. Although both of these ideas provide transparency for automated decision making, most methodologies focus on either using the modal-gradients (model- dependent) or ignoring the model internal states and reasoning with a model's behavior/outcome (model-agnostic) to instances. In this work, we propose a unified explanation approach that given an instance combines both model-dependent and agnostic explanations to produce an explanation set. The generated explanations are not only consistent in the neighborhood of a sample but can highlight causal relationships between image content and the outcome. We use Wireless Capsule Endoscopy (WCE) domain to illustrate the effectiveness of our explanations. The saliency maps generated by our approach are comparable or better on the softmax information score

A Comprehensive Analysis of AI Biases in DeepFake Detection With Massively Annotated Databases

In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably. However, there is an increased concern that these models and training databases might be biased and, thus, cause Deepfake detectors to fail. In this work, we investigate the bias issue caused by public Deepfake datasets by (a) providing large-scale demographic and non-demographic attribute annotations of 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets. The investigation analyses the influence of a large variety of distinctive attributes (from over 65M labels) on the detection performance, including demographic (age, gender, ethnicity) and non-demographic (hair, skin, accessories, etc.) information. The results indicate that investigated databases lack diversity and, more importantly, show that the utilised Deepfake detection backbone models are strongly biased towards many investigated attributes. The Deepfake detection backbone methods, which are trained with biased datasets, might output incorrect detection results, thereby leading to generalisability, fairness, and security issues. We hope that the findings of this study and the annotation databases will help to evaluate and mitigate bias in future Deepfake detection techniques. The annotation datasets and the corresponding code are publicly available

Kampledelse fra en fotballdommers perspektiv

Denne oppgaven omhandler det å være en fotballdommer med søkelys på selve managementet av de menneskene som er med på kampene i form av spillere, ledere, meddommere og annet støtteapparat. Hovedpoenget vil være å se på hvordan dommere selv oppfatter sin egen rolle og hvordan regeladministrasjon og kampledelse er en del av dette. Ved å svare på spørsmålene: (i) hvordan forstår dommere sin egen rolle, (ii) hvordan påvirker denne forståelsen hvordan fotballdommere leder kamper og (iii) hva påvirker denne forståelsen? Det er flere mulige måter å gjøre dette på, hvor man ser på kampledelsen, regeladministrasjon og hvordan dette skal gjøres i praksis (Unkelbach & Memmert, 2008, p. 398). Rolleforståelse beskrevet av Wrzesniewski & Dutton (2001) om hvordan mennesker kan skape sine egne roller og hvordan disse gjennomføres i det daglige har hovedtyngden i denne oppgaven. Til slutt er det også kommet frem en del interessante funn som viser at dommerne blant annet er veldig opptatte av det å bli oppfattet med respekt, og at de ser på sin egen rolle som essensiell for at kampene skal kunne gjennomføres på en god og trygg måte. Det er også kommet frem at selv om dommerne selv kan definere sin rolle, er det viktig å ikke bli låst til den ettersom det i hovedsak er lagene og kampen som velger om dommerne skal lede med regel-anvendelse eller kampledelse. Det som påvirker hvordan dommerne ser på sin egen rolle ser ut til å være hvilke mål dommerne har, hvilken erfaring de sitter inne med, hvilke personlige egenskaper som er gjeldende for hver dommer og hvilket nivå dommerne er på. For å koble jobb-forming blant fotballdommere opp mot ledelsesfaget mer generelt, kan ledere lære noe om hvordan dommere håndterer frustrerte spillere og dermed trekke dette over til hvordan de selv kan lede sine ansatte. Samt det å kunne ta vanskelige avgjørelser under stort press fra omverdenen og det å håndtere disse følelsene. Det er også slik at dommerne er synlige i sin jobb i form av at dommerne er de alle ser på når det skjer noe i en fotballkamp. Dette kan være slik som når et mål scores så vil publikum og spillere se på dommerne for å se om dette godkjennes