The flickering lights of content playing on TV screens in our living rooms are an all too familiar sight at night --- and one that many of us have paid little attention to with regards to the amount of information these diffusions may leak to an inquisitive outsider. In this paper, we introduce an attack that exploits the emanations of changes in light (e.g., as seen through the windows and recorded over 70 meters away) to reveal the programs we watch. Our empirical results show that the attack is surprisingly robust to a variety of noise signals that occur in real-world situations, and moreover, can successfully identify the content being watched among a reference library of tens of thousands of videos within several seconds. The robustness and efficiency of the attack can be attributed to the use of novel feature sets and an elegant online algorithm for performing index-based matches.

We explore the robustness and usability of moving-image object recognition (video) CAPTCHAS, designing and implementing automated attacks based on computer vision techniques. Our approach is suitable for broad classes of moving-image CAPTCHAS involving rigid objects. We first present an attack that defeats instances of such a CAPTCHA (NuCaptcha) representing the state-of-the-art, involving dynamic text strings called codewords. We then consider design modifications to mitigate the attacks (e.g., overlapping characters more closely, randomly changing the font of individual characters, or even randomly varying the number of characters in the codeword). We implement the modified CAPTCHAS and test if designs modified for greater robustness maintain usability. Our lab-based studies show that the modified captchas fail to offer viable usability, even when the captcha strength is reduced below acceptable targets. Worse yet, our GPU-based implementation shows that our automated approach can decode these captchas faster than humans can, and we can do so at a relatively low cost of roughly 50 cents per 1,000 captchas solved based on Amazon EC2 rates circa 2012. To further demonstrate the challenges in designing usable captchas, we also implement and test another variant of moving text strings using the known emerging images concept. This variant is resilient to our attacks and also offers similar usability to commercially available approaches. We explain why fundamental elements of the emerging images idea resist our current attack where others fail.

Of late, threats enabled by the ubiquitous use of mobile devices have drawn much interest from the research community. However, prior threats all suffer from a similar, and profound, weakness - namely the requirement that the adversary is either within visual range of the victim (e.g., to ensure that the pop-out events in reflections in the victim's sunglasses can be discerned) or is close enough to the target to avoid the use of expensive telescopes. In this paper, we broaden the scope of the attacks by relaxing these requirements and show that breaches of privacy are possible even when the adversary is around a corner. The approach we take overcomes challenges posed by low image resolution by extending computer vision methods to operate on small, high-noise, images. Moreover, our work is applicable to all types of keyboards because of a novel application of fingertip motion analysis for key-press detection. In doing so, we are also able to exploit reflections in the eyeball of the user or even repeated reflections (i.e., a reflection of a reflection of the mobile device in the eyeball of the user). Our empirical results show that we can perform these attacks with high accuracy, and can do so in scenarios that aptly demonstrate the realism of this threat.

We investigate the implications of the ubiquity of personal mobile devices and reveal new techniques for compromising the privacy of users typing on virtual keyboards. Specifically, we show that so-called compromising reflections (in, for example, a victim's sunglasses) of a device's screen are sufficient to enable automated reconstruction, from video, of text typed on a virtual keyboard. Through the use of advanced computer vision and machine learning techniques, we are able to operate under extremely realistic threat models, in real-world operating conditions, which are far beyond the range of more traditional OCR-based attacks. In particular, our system does not require expensive and bulky telescopic lenses: rather, we make use of off-the-shelf, handheld video cameras. In addition, we make no limiting assumptions about the motion of the phone or of the camera, nor the typing style of the user, and are able to reconstruct accurate transcripts of recorded input, even when using footage captured in challenging environments (e.g., on a moving bus). To further underscore the extent of this threat, our system is able to achieve accurate results even at very large distances-up to 61 m for direct surveillance, and 12 m for sunglass reflections. We believe these results highlight the importance of adjusting privacy expectations in response to emerging technologies.