Attacking image classification based on Bag-of-Visual-Words

Nowadays, with the widespread diffusion of online image databases, the possibility of easily searching, browsing and filtering image content is more than an urge. Typically, this operation is made possible thanks to the use of tags, i.e., textual representations of semantic concepts associated to the images. The tagging process is either performed by users, who manually label the images, or by automatic image classifiers, so as to reach a broader coverage. Typically, these methods rely on the extraction of local descriptors (e.g., SIFT, SURF, HOG, etc.), the construction of a suitable feature-based representation (e.g., bag-of-visual words), and the use of supervised classifiers (e.g., SVM). In this paper, we show that such a classification procedure can be attacked by a malicious user, who might be interested in altering the tags automatically suggested by the classifier. This might be used, for example, by an attacker who is willing to avoid the automatic detection of improper material in a parental control system. More specifically, we show that it is possible to modify an image in order to have it associated to the wrong class, without perceptually affecting the image visual quality. The proposed method is validated against a well known image dataset, and results prove to be promising, highlighting the need to jointly study the problem from the standpoint of both the analyst and the attacker

Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input

| openaire: EC/H2020/637991/EU//COMPUTED | openaire: EC/FP7/335545/EU//CapRealReal-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing. However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately. Many previous approaches resort to complex multi-camera setups to remedy the occlusion problem and often employ expensive segmentation and optimization steps which makes real-time tracking impossible. In this paper, we propose a real-time solution that uses a single commodity RGB-D camera. The core of our approach is a 3D articulated Gaussian mixture alignment strategy tailored to hand-object tracking that allows fast pose optimization. The alignment energy uses novel regularizers to address occlusions and hand-object contacts. For added robustness, we guide the optimization with discriminative part classification of the hand and segmentation of the object. We conducted extensive experiments on several existing datasets and introduce a new annotated hand-object dataset. Quantitative and qualitative results show the key advantages of our method: speed, accuracy, and robustness.Peer reviewe