48 research outputs found
Pooled Steganalysis in JPEG: how to deal with the spreading strategy?
International audienceIn image pooled steganalysis, a steganalyst, Eve, aims to detect if a set of images sent by a steganographer, Alice, to a receiver, Bob, contains a hidden message. We can reasonably assess that the steganalyst does not know the strategy used to spread the payload across images. To the best of our knowledge, in this case, the most appropriate solution for pooled steganalysis is to use a Single-Image Detector (SID) to estimate/quantify if an image is cover or stego, and to average the scores obtained on the set of images. In such a scenario, where Eve does not know the spreading strategies, we experimentally show that if Eve can discriminate among few well-known spreading strategies, she can improve her steganalysis performances compared to a simple averaging or maximum pooled approach. Our discriminative approach allows obtaining steganalysis efficiencies comparable to those obtained by a clairvoyant, Eve, who knows the Alice spreading strategy. Another interesting observation is that DeLS spreading strategy behaves really better than all the other spreading strategies. Those observations results in the experimentation with six different spreading strategies made on Jpeg images with J-UNIWARD, a state-of-the-art Single-Image-Detector, and a dis-criminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various spreading strategies
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
Malware detectors based on deep learning (DL) have been shown to be
susceptible to malware examples that have been deliberately manipulated in
order to evade detection, a.k.a. adversarial malware examples. More
specifically, it has been show that deep learning detectors are vulnerable to
small changes on the input file. Given this vulnerability of deep learning
detectors, we propose a practical defense against adversarial malware examples
inspired by randomized smoothing. In our work, instead of employing Gaussian or
Laplace noise when randomizing inputs, we propose a randomized ablation-based
smoothing scheme that ablates a percentage of the bytes within an executable.
During training, our randomized ablation-based smoothing scheme trains a base
classifier based on ablated versions of the executable files. At test time, the
final classification for a given input executable is taken as the class most
commonly predicted by the classifier on a set of ablated versions of the
original executable. To demonstrate the suitability of our approach we have
empirically evaluated the proposed ablation-based model against various
state-of-the-art evasion attacks on the BODMAS dataset. Results show greater
robustness and generalization capabilities to adversarial malware examples in
comparison to a non-smoothed classifier
On the trade-off between compression efficiency and distortion of a new compression algorithm for multichannel EEG signals based on singular value decomposition
In this article we investigate the trade-off between the compression ratio and distortion of a recently published compression technique specifically devised for multichannel electroencephalograph (EEG) signals. In our previous paper, we proved that, when singular value decomposition (SVD) is already performed for denoising or removing unwanted artifacts, it is possible to exploit the same SVD for compression purpose by achieving a compression ratio in the order of 10 and a percentage root mean square distortion in the order of 0.01 %. In this article, we successfully demonstrate how, with a negligible increase in the computational cost of the algorithm, it is possible to further improve the compression ratio by about 10 % by maintaining the same distortion level or, alternatively, to improve the compression ratio by about 50 % by still maintaining the distortion level below the 0.1 %
Steady-State Performance of an Adaptive Combined MISO Filter Using the Multichannel Affine Projection Algorithm
The combination of adaptive filters is an effective approach to improve filtering performance. In this paper, we investigate the performance of an adaptive combined scheme between two adaptive multiple-input single-output (MISO) filters, which can be easily extended to the case of
multiple outputs. In order to generalize the analysis, we consider the multichannel affine projection
algorithm (APA) to update the coefficients of the MISO filters, which increases the possibility of
exploiting the capabilities of the filtering scheme. Using energy conservation relations, we derive
a theoretical behavior of the proposed adaptive combination scheme at steady state. Such analysis
entails some further theoretical insights with respect to the single channel combination scheme.
Simulation results prove both the validity of the theoretical steady-state analysis and the effectiveness
of the proposed combined scheme.The work of Danilo Comminiello, Michele Scarpiniti and Aurelio Uncini has been supported by the
project: “Vehicular Fog energy-efficient QoS mining and dissemination of multimedia Big Data streams (V-FoG
and V-Fog2)”, funded by Sapienza University of Rome Bando 2016 and 2017. The work of Michele Scarpiniti
and Aurelio Uncini has been also supported by the project: “GAUChO – A Green Adaptive Fog Computing and
networking Architectures” funded by the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando
2015, grant 2015YPXH4W_004. The work of Luis A. Azpicueta-Ruiz is partially supported by the Spanish Ministry
of Economy and Competitiveness (under grant DAMA (TIN2015-70308-REDT) and grants TEC2014-52289-R and
TEC2017-83838-R), and by the European Union
Detection and restoration of click degraded audio based on high-order sparse linear prediction
Clicks are short-duration defects that affect most archived audio media. Linear prediction (LP) modeling for the representation and restoration of audio signals that have been corrupted by click degradation has been extensively studied. The use of high-order sparse linear prediction for the restoration of clickdegraded audio given the time location of samples affected by click degradation has been shown to lead to significant restoration improvement over conventional LP-based approaches. For the practical usage of such methods, the identification of the time location of samples affected by click degradation is critical. High-order sparse linear prediction has been shown to lead to better modeling of audio resulting in better restoration of click degraded archived audio. In this paper, the use of high-order sparse linear prediction for the detection and restoration of click degraded audio is proposed. Results in terms of click duration estimation, SNR improvement and perceptual audio quality show that the proposed approach based on high-order sparse linear prediction leads to better performance compared to state of the art LP-based approaches. 
Blind fractionally spaced channel equalization for shallow water PPM digital communications links
Underwater acoustic digital communications suffer from inter-symbol interference deriving from signal distortions caused by the channel propagation. Facing such kind of impairment becomes particularly challenging when dealing with shallow water scenarios characterized by short channel coherence time and large delay spread caused by time-varying multipath effects. Channel equalization operated on the received signal represents a crucial issue in order to mitigate the effect of inter-symbol interference and improve the link reliability. In this direction, this contribution presents a preliminary performance analysis of acoustic digital links adopting pulse position modulation in severe multipath scenarios. First, we show how the spectral redundancy offered by pulse position modulated signals can be fruitfully exploited when using fractional sampling at the receiver side, which is an interesting approach rarely addressed by the current literature. In this context, a novel blind equalization scheme is devised. Specifically, the equalizer is blindly designed according to a suitably modified Bussgang scheme in which the zero-memory nonlinearity is replaced by a M-memory nonlinearity, M being the pulse position modulation order. Numerical results not only confirm the feasibility of the technique described here, but also assess the quality of its performance. An extension to a very interesting complex case is also provided
Analysis of Score-Level Fusion Rules for Deepfake Detection
Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and fusion techniques can reduce this issue. In this paper, we exploit the complementarity of different individual classifiers and evaluate which fusion rules are best suited to increase the generalization capacity of modern deepfake detection systems. We also give some insights to designers for selecting the most appropriate approach
Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network
Quality inspection applications in industry are required to move towards a
zero-defect manufacturing scenario, withnon-destructive inspection and
traceability of 100 % of produced parts. Developing robust fault detection and
classification modelsfrom the start-up of the lines is challenging due to the
difficulty in getting enough representative samples of the faulty patternsand
the need to manually label them. This work presents a methodology to develop a
robust inspection system, targeting thesepeculiarities, in the context of solar
cell manufacturing. The methodology is divided into two phases: In the first
phase, an anomalydetection model based on a Generative Adversarial Network
(GAN) is employed. This model enables the detection and localizationof
anomalous patterns within the solar cells from the beginning, using only
non-defective samples for training and without anymanual labeling involved. In
a second stage, as defective samples arise, the detected anomalies will be used
as automaticallygenerated annotations for the supervised training of a Fully
Convolutional Network that is capable of detecting multiple types offaults. The
experimental results using 1873 EL images of monocrystalline cells show that
(a) the anomaly detection scheme can beused to start detecting features with
very little available data, (b) the anomaly detection may serve as automatic
labeling in order totrain a supervised model, and (c) segmentation and
classification results of supervised models trained with automatic labels
arecomparable to the ones obtained from the models trained with manual labels.Comment: 20 pages, 10 figures, 6 tables. This article is part of the special
issue "Condition Monitoring, Field Inspection and Fault Diagnostic Methods
for Photovoltaic Systems" Published in MDPI - Sensors: see
https://www.mdpi.com/journal/sensors/special_issues/Condition_Monitoring_Field_Inspection_and_Fault_Diagnostic_Methods_for_Photovoltaic_System