Merylin Monaro

The detection of faked identity using unexpected questions and mouse dynamics

The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent’s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to “build” and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee.

Identity Verification Using a Kinematic Memory Detection Technique

We present a new method that allows the identification of false self-declared identity, based on indirect measures of the memories relating the affirmed personal details. This method exploits kinematic analysis of mouse as implicit measure of deception, while the user is answering to personal information. Results show that using mouse movement analysis, it is possible to reach a high rate of accuracy in detecting the veracity of self-declared identities. In fact, we obtained an average accuracy of 88 % in the classification of single answers as truthful or untruthful, that corresponds overall to 9.7/10 participants correctly classified as true tellers or liars. The advantage of this method is that it does not requires any knowledge about the real identity of the declarant.

Covert lie detection using keyboard dynamics

Identifying the true identity of a subject in the absence of external verification criteria (documents, DNA, fingerprints, etc.) is an unresolved issue. Here, we report an experiment on the verification of fake identities, identified by means of their specific keystroke dynamics as analysed in their written response using a computer keyboard. Results indicate that keystroke analysis can distinguish liars from truth tellers with a high degree of accuracy - around 95% - thanks to the use of unexpected questions that efficiently facilitate the emergence of deception clues.

How Human-Mouse Interaction can Accurately Detect Faked Responses About Identity

Identity verification is nowadays a very sensible issue. In this paper, we proposed a new tool focused on human-mouse interaction to detect fake responses about identity. Experimental results showed that this technique is able to detect fake responses about identities with an accuracy higher than 95%. In addition to a high sensitivity, the described methodology exceeds the limits of the biometric measures currently available for identity verification and the constraints of the traditional lie detection cognitive paradigms. Thanks to the many advantages offered by this technique, its application looks promising especially in field of national and global security as anti-terrorist measure. This paper represents an advancement in the knowledge of symbiotic systems demonstrating that human-machine interaction may be well integrated into security systems.

Type Me the Truth!: Detecting Deceitful Users via Keystroke Dynamics

In this paper, we propose a novel method, based on keystroke dynamics, to distinguish between fake and truthful personal information written via a computer keyboard. Our method does not need any prior knowledge about the user who is providing data. To our knowledge, this is the first work that associates the typing human behavior with the production of lies regarding personal information. Via experimental analysis involving 190 subjects, we assess that this method is able to distinguish between truth and lies on specific types of autobiographical information, with an accuracy higher than 75%. Specifically, for information usually required in online registration forms (e.g., name, surname and email), the typing behavior diverged significantly between truthful or untruthful answers. According to our results, keystroke analysis could have a great potential in detecting the veracity of self-declared information, and it could be applied to a large number of practical scenarios requiring users to input personal data remotely via keyboard.

The Online Identity Detection via Keyboard Dynamics

Around 50% of the world population is now active on internet, often subscribing websites, social networks or other online services. In this scenario, the issue of online faked identities is more and more present, with the phenomena of identity alteration, identity theft and identity fraud. To date, there are no systems able to detect people who subscribe or authenticate an online service with faked personal information. Moreover, the existing validated lie detection techniques are not suitable to be applied in the online environment. Starting from a previous study, this paper investigates the possibility to detect faked identities recording keystroke dynamics, while the user is filling an online subscription form with personal – real or faked, information. Cognitively overloading liars through few unexpected questions, we demonstrated that it is possible to recognize the deceivers with an accuracy of 85%. To automatically detect liars, three machine-learning classifiers were trained on 40 liars and 40 truth-tellers, and tested on 10 unseen liars and 10 truth-tellers. Liars have proved to be distinguishable from truth-tellers as they make more errors and are slower in typing unexpected information about their identity.

The Detection of Malingering: A New Tool to Identify Made-Up Depression

Major depression is a high-prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double-choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine-learning algorithms, comprises 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine-learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks compared to both truth-tellers and liars. Machine-learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.

Detection of Malingering in Psychic Damage Ascertainment

Malingering is the intentional feigning or exaggeration of physical or psychological symptoms. Since the beginning of 1900 malingering detection has been one of the main challenges in medico-legal practice and in particular in psychiatric and cognitive assessment, as behavioral symptoms are very easy to produce, so that the need for specific tools and strategies for malingering detection is crucial. Although several tools and strategies are available, conclusions are often derived from mere subjective impressions and in many cases they lead to misclassifications. Here we present a non-exhaustive review of strategies for the detection of malingering, starting from the logic underlying a qualitative analysis of symptoms, to validated tools specifically designed to detect attempts at simulating or exaggerating psychopathological, psychiatric or cognitive diseases. Finally, we describe two recent approaches to the malingering detection problem. These approaches are grounded on the analysis of the reaction-times and on the dynamic analysis of kinematic features of mouse trajectories while an examinee is answering to double-choice questions.

You Are How You Play: Authenticating Mobile Users via Game Playing

Nowadays, user authentication on mobile devices is principally based on a secret (e.g., password, PIN), while recently two-factors authentication methods have been proposed to make more secure such secret-based methods. Two-factors authentication methods typically combine knowledge factors with user’s characteristics or possessions, obtaining high authentication performances. In this paper, we propose a novel two-factors authentication method based on users’ cognitive skills. Cognitive abilities are caught through the users’ performance to small games, which replicated the classical attentional paradigms of cognitive psychology. In particular, we introduced three games that rely on selective attention, attentional switch and Stroop effect. While users were solving a game on their smartphones, we collected cognitive performance (in terms of accuracy and reaction times), touch features (interactions with touch screen), and sensors features (data from accelerometer and gyroscope). Results show that our cognitive-based games can be used as a two-factors authentication mechanism on smartphones. Relying on touch and sensors features as behavior biometrics, we are able to achieve an authentication accuracy of \(97\%\), with a Equal Error Rate of \(1.37\%\).