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==== Compromising reflections ====
What is displayed by the monitor is reflected on the environment. The time-varying diffuse reflections of the light emitted by a CRT monitor can be exploited to recover the original monitor image.<ref name="[
The technique exploits reflections of the screen’s optical emanations in various objects that one commonly finds in close proximity to the screen and uses those reflections to recover the original screen content. Such objects include eyeglasses, tea pots, spoons, plastic bottles, and even the eye of the user. This attack can be successfully mounted to spy on even small fonts using inexpensive, off-the-shelf equipment (less than 1500 dollars) from a distance of up to 10 meters. Relying on more expensive equipment allowed to conduct this attack from over 30 meters away, demonstrating that similar attacks are feasible from the other side of the street or from a close-by building.<ref name="[
Many objects that may be found at a usual workplace can be exploited to retrieve information on a computer’s display by an outsider.<ref name="[Back4]">[[#Back2|Backes, 2008, p.4]]</ref> Particularly good results were obtained from reflections in a user’s eyeglasses or a tea pot located on the desk next to the screen. Reflections that stem from the eye of the user also provide good results. However, eyes are harder to spy on at a distance because they are fast-moving objects and require high exposure times. Using more expensive equipment with lower exposure times helps to remedy this problem.<ref name="[Back5]">[[#Back2|Backes, 2008, p.11]]</ref>
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With acoustic emanations, an attack that recovers what a dot-matrix printer processing English text is printing is possible. It is based on a record of the sound the printer makes, if the microphone is close enough to it. This attack recovers up to 72% of printed words, and up to 95% if knowledge about the text are done, with a microphone at a distance of 10 cm from the printer.<ref name="[Back1]">[[#Back1|Backes, 2010, p.1]]</ref>
After an upfront training phase ("a" in the picture below), the attack ("b" in the picture below) is fully automated and uses a combination of machine learning, audio processing, and speech recognition techniques, including spectrum features, Hidden Markov Models and linear classification.<ref name="[Back2]"
A training phase was conducted where words from a dictionary are printed and characteristic sound features of these words are extracted and stored in a database. The trained characteristic features was used to recognize the printed English text.<ref name="[Back2]"/> But, this task is not trivial. Major challenges include :
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