All-optical multilevel physical unclonable functions.

Disordered photonic structures are promising for the realization of physical unclonable functions-physical objects that can overcome the limitations of conventional digital security and can enable cryptographic protocols immune against attacks by future quantum computers. The physical configuration of traditional physical unclonable functions is either fixed or can only be permanently modified, allowing one token per device and limiting their practicality. Here we overcome this limitation by creating reconfigurable structures made by light-transformable polymers in which the physical structure of the unclonable function can be reconfigured reversibly. Our approach allows the simultaneous coexistence of multiple physical unclonable functions within one device. The physical transformation is done all-optically in a reversible and spatially controlled fashion, allowing the generation of more complex keys. At the same time, as a set of switchable individual physical unclonable functions, it enables the authentication of multiple clients and allows for the practical implementations of quantum secure authentication and nonlinear generators of cryptographic keys.

Detection Games under Fully Active Adversaries.

We study a binary hypothesis testing problem in which a defender must decide whether a test sequence has been drawn from a given memoryless source P 0 , while an attacker strives to impede the correct detection. With respect to previous works, the adversarial setup addressed in this paper considers an attacker who is active under both hypotheses, namely, a fully active attacker, as opposed to a partially active attacker who is active under one hypothesis only. In the fully active setup, the attacker distorts sequences drawn both from P 0 and from an alternative memoryless source P 1 , up to a certain distortion level, which is possibly different under the two hypotheses, to maximize the confusion in distinguishing between the two sources, i.e., to induce both false positive and false negative errors at the detector, also referred to as the defender. We model the defender-attacker interaction as a game and study two versions of this game, the Neyman-Pearson game and the Bayesian game. Our main result is in the characterization of an attack strategy that is asymptotically both dominant (i.e., optimal no matter what the defender's strategy is) and universal, i.e., independent of P 0 and P 1 . From the analysis of the equilibrium payoff, we also derive the best achievable performance of the defender, by relaxing the requirement on the exponential decay rate of the false positive error probability in the Neyman-Pearson setup and the tradeoff between the error exponents in the Bayesian setup. Such analysis permits characterizing the conditions for the distinguishability of the two sources given the distortion levels.

Covert Task Embedding: Turning a DNN Into an Insider Agent Leaking Out Private Information.

We present the covert task embedding (CTE) attack, a new general threat affecting deep neural networks (DNNs). The new attack consists in hiding a malicious privacy-sensitive task within a seemingly innocuous network, in such a way that the result of the malicious task is delivered together with the legitimate output in a stealthy way. The result of the covert task is further protected by requiring that its extraction depends on a secret key shared by the embedder and the detector. We demonstrate the feasibility of the CTE attack in various settings, wherein a face-based age estimation DNN is trained in such a way as to also detect the gender (binary classification task) or ethnicity (multiclassification task) of the framed individual and stealthily pass along such information together with the estimated age. The results of the experiments we carried out show that, in all cases, the gender and ethnicity information can be reliably extracted without impairing the accuracy of the age estimation functionality. Despite the simplicity of the estting considered in the brief, our experiments show the feasibility of the CTE attack, thus calling for the development of suitable remedies against it.

VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents.

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

Visuohaptic discrimination of 3D gross shape.

Human sensitivity to 3D gross shape changes was measured for the visual and haptic sensory channels. Three volume-invariant affine transformations were defined: compressing, shearing and stretching. Participants discriminated a reference 3D object (cube or sphere) from its deformed shape under three experimental conditions: visual only (on a computer monitor), haptic only (through a point-contact force-feedback device) and visuohaptic simulations. The results indicate that vision is more sensitive to gross shape changes than point-based touch, and that vision dominated in the visuohaptic condition. In the haptic alone condition, thresholds were higher for shearing and stretching than for compressing. Thresholds were otherwise similar for the three transformations in the vision only or visuohaptic conditions. These trends were similar for the two shapes tested. A second experiment, conducted under similar conditions but preventing participants from manipulating object orientations, verified that the main conclusion of our research still holds when visual inspection can rely only on a single perspective view of the object. Our earlier studies on 3D visuohaptic watermarking showed that the haptic channel is more sensitive to surface texture and roughness changes than vision. The thresholds from the present and our earlier studies can potentially be used as the upper limits for selecting watermark strengths in order to ensure watermark imperceptibility in a 3D visuohaptic watermarking system.

A full-reference quality metric for geometrically distorted images.

In multimedia applications, there has been an increasing interest in the use of quality measures based on human perception; however, research has not dealt with distortions due to geometric transformations. In this paper, we propose a method to objectively assess the perceptual quality of geometrically distorted images, based on image features processed by human vision. The proposed approach is a full-reference image quality metric focusing on the problem of local geometric distortions and is based on the use of Gabor filters that have received considerable attention because the characteristics of certain cells in the visual cortex of some mammals can be approximated by these filters. The novelty of the proposed technique is that it considers both the displacement field describing the distortion and the structure of the image. The experimental results show the good performances of the proposed metric.