We study how potential attackers can identify accounts on different social network sites that all belong to the same user, exploiting only innocuous activity that inherently comes with posted content. We examine three specific features on Yelp, Flickr, and Twitter: the geo-location attached to a user's posts, the timestamp of posts, and the user's writing style as captured by language models. We show that among these three features the location of posts is the most powerful feature to identify accounts that belong to the same user in different sites. When we combine all three features, the accuracy of identifying Twitter accounts that belong to a set of Flickr users is comparable to that of existing attacks that exploit usernames. Our attack can identify 37% more accounts than using usernames when we instead correlate Yelp and Twitter. Our results have significant privacy implications as they present a novel class of attacks that exploit users' tendency to assume that, if they maintain different personas with different names, the accounts cannot be linked together; whereas we show that the posts themselves can provide enough information to correlate the accounts.

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The following article describes an approach to determine the geo-coordinates of the recording place of Flickr videos based on both textual metadata and visual cues. The system is tested on the MediaEval 2010 Placing Task evaluation data, which consists of 5091 unfiltered test videos. The system presented in this article is less complex, uses less training data, and is at the same time more accurate than the best system presented in the evaluation in August 2010. The performance peaks at being able to classify 14 % of the videos with less than 10 m accuracy. The article describes the realization of the system, analyses of the different uses of multimodal cues and gazetteer information.

In this work, we have investigated the performance of 2D Gabor features (known as spectro-temporal features) for speaker recognition. Gabor features have been used mainly for automatic speech recognition (ASR), where they have yielded improvements. We explored different Gabor feature implementations, along with different speaker recognition approaches, on ROSSI [1] and NIST SRE08 databases. Using the noisy ROSSI database, the Gabor features performed as well as the MFCC features standalone, and score-level combination of Gabor and MFCC features resulted in an 8% relative EER improvement over MFCC features standalone. These results demonstrated the value of both spectral and temporal information for feature extraction, and the complementarity of Gabor features to MFCC features.

We have performed city-verification of videos based on the videos' audio and metadata, using videos from the MediaEval Placing Task's video set, which contain consumer-produced videos "fromthe-wild". 18 cities were used as targets, for which acoustic and language models were trained, and against which test videos were scored. We have obtained the first known results for the city verification task, with an EER minimum of 21.8%, suggesting that ∼80% of test videos, when tested against a correct target city, were identified as belonging to that city. This result is well above-chance, even as the videos contained very few city-specific audio and metadata features. We have also demonstrated the complementarity of audio and metadata for this task.