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Priori FAR=10^-3. Argenti’s filter has a significative lower FRR for Samsung and Olympus. In the general the two filters show a comparable behavior. Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44% Ten to the minus three The LP filter has the worst behaviour as obviously expected. The other two filters showed a comparable behaviour: the FRR has the same order of magnitude Argenti’s filter has a significative lower FRR for Samsung and Olympus In the other case does not exhibit a considerable improvement in the results Because the filter depends on the reliability of the parameters estimation 14 Results- denoising filter comparisonCorrelation values for 20 images from a Olympus FE120 with 5 fingerprints. LP filter Mihcak filter Argenti filter Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44% LP filter Mihcak filter Argenti filter The higher values are those related to the correlation between the noise residual of the Olympus FE120 images and its fingerprint. The distributions of the correlation values are well separated in the Argenti cases. Correlation values for 20 images from a Olympus FE120 with 5 fingerprints of various cameras are pictured FOR Mihcak (left) and Argenti (right) filters respectively included 15 Conclusions Future TrendsIntroducing a novel filter for the estimation of PRNU. An analysis on different kinds of denoising filters for PRNU extraction as been presented. Experimental results on camera identification have been provided. Future Trends Improve methodology extraction for PRNU. Force parameter in the Argenti noise model and repeat the experiments. In the end I show you a method for the source idetinfication that use Sensor Noise to determine what Cam Shot the images.The future trends are: Overlap Introdotto un nuovo filtro usato in un’altra area di ricerca Modello paragonabile a quello del miodello di acquisizione Impove parametrs estimation of the Argenti

An anonymous reader quotes a report from ZDNet: This year, artificial intelligence will be dominated by the maturation of AI code as corporate "workers" that can take over corporate processes and be managed just like employees, according to a year-outlook blog post disseminated by investment bank Goldman Sachs featuring its chief information officer, Marco Argenti. "The capabilities of AI models to plan and execute complex, long-running tasks on humans' behalf will begin to mature," writes Argenti. "This will create the conditions for companies to eventually 'employ' and train AI workers to be part of hybrid teams of humans and AIs working together." "There's a great opportunity for capital to move towards the application layer, the toolset layer," says Goldman Sachs CIO Marco Argenti. "I think we will see that shift happening, most likely as early as next year." Argenti predicts that corporate HR offices will have to manage "human and machine resources," and there may even be AI "layoffs" as programs are replaced by more highly capable versions. [...] Among other predictions offered by Argenti is that the most-capable AI models will be like PhD graduates -- so-called expert AI systems that have "industry-specific knowledge" for finance, medicine, etc. [...] "The intersection of LLMs and robotics will increasingly bring AI into, and enable it to experience, the physical world, which will help enable reasoning capabilities for AI," he writes. Argenti sees "responsible AI" increasing in importance as a board-room priority in 2025, and, in something of a repeat of last year's predictions, he expects that the largest generative AI models -- the "frontier" models of OpenAI and others -- will become the province of only a handful of institutions with budgets large enough to pursue their enormous training costs. That is the "Formula One" version of AI, where the "engines" of AI are made by a handful of powerful providers. Everyone else will work on smaller-model development, Argenti predicts. Further reading: Nvidia's Huang Says That IT Will 'Become the HR of AI Agents'Read more of this story at Slashdot. Click here to read full news..

Test image imm(k) is taken by camera A? camera A Finally to identify what camera has taken that image we need to exctract PRNU from that image as done before and then we performe a correlation between this PRNU and all the available Fingerprints. The fingerprint whose correlaction is higher than predefined threshold is supposed to be the camera that shoot the image. imm(k) is taken by camera A 9 Digital Camera Identificationdenoising filter The digital filter has an important role for PRNU extraction! Comparison and analysis of two denoising filters: Previously used Mihçak Filter [1] additive noise model ‏ Novel Argenti-Alparone Filter [2] signal-dependent noise model Fingerprint estimation from N images (no smooth images)‏ Fingerprint detection: correlation; given an image we calculate the noise pattern and then correlated with the known reference pattern from a set of cameras. Decision: threshold, Neymann Pearson criterion FAR=10^-3 Mihack filter usato nei lavoro di Fridrich per la stima del PRNU Argenti specke noise removal (SAR images) Basato su modello di rumore solo additivo (modello + semplice) Idea: usare un filtro basato su un modello di rumore + complesso: signal dependent cioè……I=,…. Modello paragonabile a quello del processo di acquisizione di un digital camera: uguale quando alpha=1 Modello + generico e puà essere ridotto al modello del processo di acquisizione Modello + complesso To extract the PRNU (fingerprint) we generally used denoising filtering in particulary in our analysis we have compare: A basic low pass filter, used like lower bound performance A mihcak Filter A Argenti-Alparone Filter All of this are filter based on Wavelet domain and different noise model. The assumption to apply our techniques is to have a camera available or N images taken by the camera [1] K. Ramchandran M. K. Mihcak, I. Kozintsev, “Spatially adaptive statistical model of wavelet image

Coefficients and its application to denoising”, 1999. [2] L. Alparone F. Argenti, G. Torricelli, “Mmse filtering of generalised signal-dependent noise in spatial and shift-invariant wavelet domain“, 2005. 10 Mihcak’s Filter additive noise model (AWGN)spatially adaptive statistical modelling of wavelet coefficients 4 level DWT (Daubechies) MAP (Maximum A Posteriori) approach to calculate the estimate of the signal variance Wiener filter in the wavelet domain LL subband For each detail subband Coeff. 11 Argenti’s Filter signal-dependent noise modelThe parameters to be estimated are: and On homogeneous pixels, log scatter plot regression line and then MMSE filter in spatial domain. MMSE (minimum mean-square error) filter in undecimated wavelet domain estimate noise free image noisy image stationary zero-mean uncorrelated random process electronics noise (AWGN) For each detail subband LL subband Noise estimate Iterative estimate Minimizzazione lineare locale errore quadratico medio Prima stima di alpha e sigmau: si riduce il carico computazionale Da test fatti il raffineanto della stima non incide nei risultati nel caso della source identification magari nello speckle ha più senso 12 Results- denoising filter comparison10 digital cameras. Data set: training-set to calculate the fingerprint: 40 images for each camera. test-set: 250 images for each camera. A low pass filter (DWT detail coefficients are set to zero) is used to provide a performance lower bound. Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44% The LP filter has the worst behaviour as obviously expected. The other two filters showed a comparable behaviour: the FRR has the same ored of magnitude Argenti’s filter has a significative lower FRR for Samsung and Olympus In the other case does not exhibit a considerable improvement in the results Because the filter depends on the reliability of the parameters estimation 13 Results- denoising filter comparisonCalculate a threshold that minimize the FRR with Neymann-Pearson criterion with a