proposed a Brain-Computer-Interface (BCI) -based attack in which an adversary is able to infer private information about a user, such as their bank or area-of-living, by analyzing the user's brain activities. However, a key limitation of the above attack is that it is intrusive, requiring user cooperation, and is thus easily detectable and can be reported to other users. In this paper, we identify and analyze a more serious threat for users of BCI devices. We propose a it subliminal attack in which the victim is attacked at the levels below his cognitive perception. Our attack involves exposing the victim to visual stimuli for a duration of 13.3 milliseconds -- a duration usually not sufficient for conscious perception. The attacker analyzes subliminal brain activity in response to these short visual stimuli to infer private information about the user. If carried out carefully, for example by hiding the visual stimuli within screen content that the user expects to see, the attack may remain undetected. As a consequence, the attacker can scale it to many victims and expose them to the attack for a long time. We experimentally demonstrate the feasibility of our subliminal attack via a proof-of-concept study carried out with 27 subjects. We conducted experiments on users wearing Electroencephalography-based BCI devices, and used portrait pictures of people as visual stimuli which were embedded within the background of an innocuous video for a time duration not exceeding 13.3 milliseconds. Our experimental results show that it is feasible for an attacker to learn relevant private information about the user, such as whether the user knows the identity of the person for which the attacker is probing.
Subliminal Face Recognition. Given that facial recognition is seriously considered as a method of authentication, it becomes even more important to test the possibilities of extracting information from facial stimuli. There is existing neuroscientific work on the subliminal perception of human faces. ERPs in response to unpleasant expressions on faces have been shown to have a higher positive amplitude than pleasant expressions. This effect shows even through very fast unmasked subliminal presentations of stimuli, at 1ms @cite_7 . This shows that visual information regarding faces can be processed and produce variance in the EEG signal even at a very subliminal level. Although in our experiment several subjects had noticed the stimuli, a presentation time as little as 1ms could still probably reveal enough information in their EEG signal to extract desired information about faces.
@cite_7
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This experiment provides brain event-related potential (ERP) evidence for differential processing of visually presented pleasant and unpleasant affectively valent words (mood adjectives) for both supraliminal (40 ms) and subliminal (unmasked, 1 ms) stimulus durations. Unpleasant words elicited a more positive amplitude than pleasant words in both durations. ERP components (P1, N1, P2, P3, and a late positive potential; LP) were measured at six electrode sites (F3, F4, P3, P4, CzPz, Oz). ERPs to subliminal stimuli demonstrated differences between pleasant and unpleasant words in the left hemisphere across all measured components. Supraliminal processing showed similar differences in the left hemisphere for early components (P1 and N1), but bilateral differences for late components (P3 and LP). Activity in the P2 time window was associated with the divergence between supraliminal and subliminal affective responses. Implications for the study of affect and consciousness are discussed. Current theories of emotion suggest that threat-related stimuli are first processed via an automatically engaged neural mechanism, which occurs outside conscious awareness. This mechanism operates in conjunction with a slower and more comprehensive process that allows a detailed evaluation of the potentially harmful stimulus (LeDoux, 1998). We drew on the Halgren and Marinkovic (1995) model to examine these processes using event-related potentials (ERPs) within a backward masking paradigm. Stimuli used were faces with fear and neutral (as baseline control) expressions, presented above (supraliminal) and below (subliminal) the threshold for conscious detection. ERP data revealed a double dissociation for the supraliminal versus subliminal perception of fear. In the subliminal condition, responses to the perception of fear stimuli were enhanced relative to neutral for the N2 "excitatory" component, which is thought to represent orienting and automatic aspects of face processing. By contrast, supraliminal perception of fear was associated with relatively enhanced responses for the late P3 "inhibitory" component, implicated in the integration of emotional processes. These findings provide evidence in support of Halgren and Marinkovic's temporal model of emotion processing, and indicate that the neural mechanisms for appraising signals of threat may be initiated, not only automatically, but also without the need for conscious detection of these signals. Emotions are time varying affective phenomena that are elicited as a result of stimuli. Videos and movies in particular are made to elicit emotions in their audiences. Detecting the viewers’ emotions instantaneously can be used to find the emotional traces of videos. In this paper, we present our approach in instantaneously detecting the emotions of video viewers’ emotions from electroencephalogram (EEG) signals and facial expressions. A set of emotion inducing videos were shown to participants while their facial expressions and physiological responses were recorded. The expressed valence (negative to positive emotions) in the videos of participants’ faces were annotated by five annotators. The stimuli videos were also continuously annotated on valence and arousal dimensions. Long-short-term-memory recurrent neural networks (LSTM-RNN) and continuous conditional random fields (CCRF) were utilized in detecting emotions automatically and continuously. We found the results from facial expressions to be superior to the results from EEG signals. We analyzed the effect of the contamination of facial muscle activities on EEG signals and found that most of the emotionally valuable content in EEG features are as a result of this contamination. However, our statistical analysis showed that EEG signals still carry complementary information in presence of facial expressions. Micro-expression recognition (MER) is a growing field of research which is currently in its early stage of development. Unlike conventional macro-expressions, micro-expressions occur at a very short duration and are elicited in a spontaneous manner from emotional stimuli. While existing methods for solving MER are largely non-deep-learning-based methods, deep convolutional neural network (CNN) has shown to work very well on such as face recognition, facial expression recognition, and action recognition. In this article, we propose applying the 3D flow-based CNNs model for video-based micro-expression recognition, which extracts deeply learned features that are able to characterize fine motion flow arising from minute facial movements. Results from comprehensive experiments on three benchmark datasets—SMIC, CASME CASME II, showed a marked improvement over state-of-the-art methods, hence proving the effectiveness of our fairly easy CNN model as the deep learning benchmark for facial MER.