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There’s a video of Gal Gadot having sex with her stepbrother on the internet. But it’s not really Gadot’s body, and it’s barely her own face. It’s an approximation, face-swapped to look like she’s performing in an existing incest-themed porn video. The video was created with a machine learning algorithm, using easily accessible materials and open-source code that anyone with a working knowledge of deep learning algorithms could put together. It's not going to fool anyone who looks closely. Sometimes the face doesn't track correctly and there's an uncanny valley effect at play, but at a glance it seems believable. It's especially striking considering that it's allegedly the work of one person—a Redditor who goes by the name 'deepfakes'—not a big special effects studio that can digitally recreate a young Princess Leia in Rogue One using CGI. Instead, deepfakes uses open-source machine learning tools like TensorFlow, which Google makes freely available to researchers, graduate students, and anyone with an interest in machine learning. Like the Adobe tool that can make people say anything, and the Face2Face algorithm that can swap a recorded video with real-time face tracking, this new type of fake porn shows that we're on the verge of living in a world where it's trivially easy to fabricate believable videos of people doing and saying things they never did. Even having sex. So far, deepfakes has posted hardcore porn videos featuring the faces of Scarlett Johansson, Maisie Williams, Taylor Swift, Aubrey Plaza, and Gal Gadot on Reddit. I’ve reached out to the management companies and/or publicists who represent each of these actors informing them of the fake videos, and will update if I hear back. Fake celebrity porn, where images are photoshopped to look like famous people are posing nude, is a years-old category of porn with an ardent fan base. People commenting and voting in the subreddit where deepfakes posts are big fans of his work. This is the latest advancement in that genre. “This is no longer rocket science.” According to deepfakes—who declined to give his identity to me to avoid public scrutiny—the software is based on multiple open-source libraries, like Keras with TensorFlow backend. To compile the celebrities’ faces, deepfakes said he used Google image search, stock photos, and YouTube videos. Deep learning consists of networks of interconnected nodes that autonomously run computations on input data. In this case, he trained the algorithm on porn videos and Gal Gadot’s face. After enough of this “training,” the nodes arrange themselves to complete a particular task, like convincingly manipulating video on the fly. Artificial intelligence researcher Alex Champandard told me in an email that a decent, consumer-grade graphics card could process this effect in hours, but a CPU would work just as well, only more slowly, over days. “This is no longer rocket science,” Champandard said. The ease with which someone could do this is frightening. Aside from the technical challenge, all someone would need is enough images of your face, and many of us are already creating sprawling databases of our own faces: People around the world uploaded 24 billion selfies to Google Photos in 2015-2016. It isn’t difficult to imagine an amateur programmer running their own algorithm to create a sex tape of someone they want to harass. Deepfakes told me he’s not a professional researcher, just a programmer with an interest in machine learning. “I just found a clever way to do face-swap,” he said, referring to his algorithm. “With hundreds of face images, I can easily generate millions of distorted images to train the network,” he said. “After that if I feed the network someone else's face, the network will think it's just another distorted image and try to make it look like the training face.” In a comment thread on Reddit, deepfakes mentioned that he is using an algorithm similar to one developed by Nvidia researchers that uses deep learning to, for example, instantly turn a video of a summer scene into a winter one. The Nvidia researchers who developed the algorithm declined to comment on this possible application. In almost all of the examples deepfakes has posted, the result isn’t perfect. In the Gadot video, a box occasionally appeared around her face where the original image peeks through, and her mouth and eyes don’t quite line up to the words the actress is saying—but if you squint a little and suspend your belief, it might as well be Gadot; other videos deepfakes have made are even more convincing. Porn performer Grace Evangeline told me over Twitter direct messages that porn stars are used to having their work spread around free to tube sites like SendVid, where the Gal Gadot fake is uploaded, without their permission. But she said that this was different. She’d never seen anything like this. “One important thing that always needs to happen is consent,” Evangeline said. “Consent in private life as well as consent on film. Creating fake sex scenes of celebrities takes away their consent. It’s wrong.” Even for people whose livelihoods involve getting in front of a camera, the violation of personal boundaries is troubling. I showed Alia Janine, a retired porn performer who was in the sex industry for 15 years, the video of Gadot. “It’s really disturbing,” she told me over the phone. “It kind of shows how some men basically only see women as objects that they can manipulate and be forced to do anything they want... It just shows a complete lack of respect for the porn performers in the movie, and also the female actresses.” I asked deepfakes whether he considered the ethical implications of this technology. Did consent, revenge porn, and blackmail enter their mind while developing this algorithm? “Every technology can be used with bad motivations, and it's impossible to stop that,” he said, likening it to the same technology that recreated Paul Walker’s post-mortem performance in Furious 7. “The main difference is how easy [it is] to do that by everyone. I don't think it's a bad thing for more average people [to] engage in machine learning research.” Ethically, the implications are “huge,” Champandard said. Malicious use of technology often can’t be avoided, but it can be countered. “We need to have a very loud and public debate,” he said. ”Everyone needs to know just how easy it is to fake images and videos, to the point where we won't able to distinguish forgeries in a few months from now. Of course, this was possible for a long time but it would have taken a lot of resources and professionals in visual effects to pull this off. Now it can be done by a single programmer with recent computer hardware.” Champandard said researchers can then begin developing technology to detect fake videos and help moderate what’s fake and what isn’t, and internet policy can improve to regulate what happens when these types of forgeries and harassment come up. “In a strange way,” this is a good thing, Champandard said. “We need to put our focus on transforming society to be able to deal with this.” Correction: This story has been updated to clarify that deepfake's algorithm is similar to the research produced by Nvidia researchers, but that there's no evidence that it's an application of their work. Sursa: https://motherboard.vice.com/en_us/article/gydydm/gal-gadot-fake-ai-porn

The previous two blog posts describe how a Stack Based Buffer Overflow vulnerability works on x86 (32 bits) Windows. In the first part, you can find a short introduction to x86 Assembly and how the stack works, and on the second part you can understand this vulnerability and find out how to exploit it. This article will present a similar approach in order to understand how it is possible to exploit this vulnerability on x64 (64 bits) Windows. First part will cover the differences in the Assembly code between x86 and x64 and the different function calling convention, and the second part will detail how these vulnerabilities can be exploited. ASM for x64 There are multiple differences in Assembly that need to be understood in order to proceed. Here we will talk about the most important changes between x86 and x64 related to what we are going to do. Articol complet: https://nytrosecurity.com/2018/01/24/stack-based-buffer-overflows-on-x64-windows/

It all started with a tweet, which seemed to resonate with people: The aim was to list blogs that specifically cover .NET internals at a low-level or to put it another way, blogs that answer the question how does feature ‘X’ work, under-the-hood. The list includes either typical posts for that blog, or just some of my favourites! Note: for a wider list of .NET and performance related blogs see Awesome .NET Performance by Adam Sitnik I wouldn’t recommend reading through the entire list, at least not in one go, your brain will probably melt. Picks some posts/topics that interest you and start with those. Finally, bear in mind that some of the posts are over 10 years old, so there’s a chance that things have changed since then (however, in my experience, the low-levels parts of the CLR are more stable). If you want to double-check the latest behaviour, you’re best option is to read the source! Community or Non-Microsoft Blogs These blogs are all written by non-Microsoft employees (AFAICT), or if they do work for Microsoft, they don’t work directly on the CLR. If I’ve missed any interesting blogs out, please let me know! Special mention goes to Sasha Goldshtein, he’s been blogging about this longer than anyone!! All Your Base Are Belong To Us by Sasha Goldshtein (@goldshtn) Generic Method Dispatch Inspecting Local Root Lifetime Virtual Method Dispatch and Object Layout Changes in CLR 4.0 Runtime Representation of Generics—Part 2 Revisiting Value Types vs. Reference Types Dissecting the code by Sergey Teplyakov (@STeplyakov) (M/S) Garbage collection and variable lifetime tracking Managed object internals, Part 1. The layout (Also part 2, part 3 and part 4) To box or not to Box? That is the question! Dissecting the new() constraint in C#: a perfect example of a leaky abstraction Adam Sitnik - .NET Performance and Reliability by Adam Sitnik (@SitnikAdam) (M/S) Value Types vs Reference Types Span Pooling large arrays with ArrayPool Collecting Hardware Performance Counters with BenchmarkDotNet Disassembling .NET Code with BenchmarkDotNet Andrey Akinshin’s blog by Andrey Akinshin (@andrey_akinshin) Measuring Performance Improvements in .NET Core with BenchmarkDotNet (Part 1) Blittable types DateTime under the hood Stopwatch under the hood TooSlowException by Konrad Kokosa (@konradkokosa) .NET Core – compilation, running, debugging How does Object.GetType() really work? Zero Garbage Collector for .NET Core The Ultimate .NET Experiment – open source project a little bit of programming by Marcin Juraszek (@mmjuraszek) (M/S) String.Split and int[] allocations Adding Matt operator to Roslyn - Syntax, Lexer and Parser (Part 2 - Binder, Part 3 - Emitter) yizhang82’s blog by Yi Zhang (@yizhang82) (M/S) Sharing .NET generic code under the hood C# value type boxing under the hood Embedding CoreCLR in your C/C++ application Timur Guev’s posts on {coding}Sight by Timur Guev (@timyrik200), also appears to have his own blog Math and Programming (in Russian) The origin of GetHashCode in .NET Aspects of Strings in .NET StringBuilder: the Past and the Future The mole is digging by Alexandr Nikitin (@nikitin_a_a) .NET Generics under the hood Hoisting in .NET Explained Hoisting in .NET Examples My Coding Place by Dudi Keleti (@dudi_ke) Object header get complicated IL Call Vs. Callvirt Instruction (Part 2) Value type methods – call, callvirt, constrained and hidden boxing Alexandre Mutel’s blog by Alexandre Mutel (@xoofx) A new stackalloc operator for reference types with CoreCLR and Roslyn Struct inheritance in C# with CoreCLR and Roslyn Microsoft Engineers The blogs below are written by the actual engineers who worked on, designed or managed various parts of the CLR, so they give a deep insight (again, if I’ve missed any blogs out, please let me know): Maoni’s WebLog - CLR Garbage Collector by Maoni Stephens Suspending and resuming threads for GC Allocating on the stack or the heap? Large Object Heap cbrumme’s WebLog by Christopher Brumme Memory Model Value Types Virtual and non-virtual A blog on coding, .NET, .NET Compact Framework and life in general.. by Abhinaba Basu .NET Just in Time Compilation and Warming up Your System Trivia: How does CLR create an OutOfMemoryException Back to basic: Series on dynamic memory management Joel Pobar’s CLR weblog - CLR Program Manager: Reflection, LCG, Generics and the type system.. by Joel Pobar CLR Type System notes CLR Generics and code sharing Explanatory notes on Rotor’s Garbage Collector CLR Profiling API Blog - Info about the Common Language Runtime’s Profiling API by David Broman (slightly niche, but still worth a read) Creating an IL-rewriting profiler Type Forwarding Metadata Tokens, Run-Time IDs, and Type Loading Books Finally, if you prefer reading off-line there are some decent books that discuss .NET Internals (Note: all links are Amazon Affiliate links): CLR via C#, 4ed by Jeffrey Richter Shared Source CLI Essentials Paperback by David Stutz, Ted Neward, Geoff Shilling Writing High-Performance .NET Code Paperback by Ben Watson Pro .NET Performance: Optimize Your C# Applications by Sasha Goldshtein All the books listed above I’ve bought copies of and read cover-to-cover, they’re fantastic resources. I’ve also been recently recommend the 2 books below, they look good and certainly the authors know their stuff, but I haven’t read them yet: The Common Language Infrastructure Annotated Standard by James S. Miller, Susann Ragsdale Essential .NET, Volume I: The Common Language Runtime by Don Box, Chris Sells Sursa: http://mattwarren.org/2018/01/22/Resources-for-Learning-about-.NET-Internals/

Today, Facebook AI Research (FAIR) open sourced Detectron — our state-of-the-art platform for object detection research. The Detectron project was started in July 2016 with the goal of creating a fast and flexible object detection system built on Caffe2, which was then in early alpha development. Over the last year and a half, the codebase has matured and supported a large number of our projects, including Mask R-CNN and Focal Loss for Dense Object Detection, which won the Marr Prize and Best Student Paper awards, respectively, at ICCV 2017. These algorithms, powered by Detectron, provide intuitive models for important computer vision tasks, such as instance segmentation, and have played a key role in the unprecedented advancement of visual perception systems that our community has achieved in recent years. Beyond research, a number of Facebook teams use this platform to train custom models for a variety of applications including augmented reality and community integrity. Once trained, these models can be deployed in the cloud and on mobile devices, powered by the highly efficient Caffe2 runtime. Our goal in open sourcing Detectron is to make our research as open as possible and to accelerate research in labs across the world. With its release, the research community will be able to reproduce our results and have access to the same software platform that FAIR uses every day. Detectron is available under the Apache 2.0 license at https://github.com/facebookresearch/Detectron We’re also releasing extensive performance baselines for more than 70 pre-trained models that are available to download from our model zoo. Sursa: https://research.fb.com/facebook-open-sources-detectron/

BTC e in downtrend, a facut double bottom, e posibil sa scada mai mult. Mie unu imi convine daca scade, pot cumpara mai mult la un pret mai bun. De revenit o sa-si revina, daca ati citi detali tehnice deja are peste 100 de noduri de lightning si numarul lor creste mereu. Cu toate astea nu e indicat sa faceti tranzactii mai mari ide 0.3/0.4 pe lightning. Tehnologia evolueaza, e posibil ca piata sa nu mai fie la fel de bully ca in Decembrie cand a fost un bull run non-stop si sa se corecteze. Sanse sunt zilnic, puteti merge pe specula sau pe long term. Your money, your choice. Idea e urmatoarea, buy the DIP, cand scade grav, porfitati de ocazie, va va fi dor de momente de genul asta.

Ma, BAC-u in pula mea inseamna minimu de cultura generala pe care trebuie sa il ai . Programarea ca programarea da macar ia-ti BAC-u sa stii si tu cine a scris Corola de minuni a lumii si alte cele... puii mei de inculti.

Telefonul este pus langa doua bobine pe ferita iar in partea dreapta se pot vedea ceva condensatori electrolitici mai mari. Asta ma duce la ideea ca acolo este blocul de alimentare iar acei condensatori sunt pentru filtraj. Zgomotul este normal pentru ca acele bobine oscileaza pe anumite frecvente. Problema cred ca este generata de condensatorii electrolitici care sunt vechi si de calitate proasta (s-a uscat electrolitul si nu mai au valorile normale). Ei oricum au abateri de peste 10% si cand sunt noi. Daca esti electronist sau te pricepi cat de cat, poti incerca sa-i inlocuiesti sa ai ce face ca hobby. Daca nu ... nu cred ca renteaza sa te chinui. O jucarie de genul o cumperi cu 20-30 de euro de pe ebay.

Corect. Am uitat să editez. Le am deja pregătite. Unde am lăsat telefoul să filmeze, din zona aia scoate acele sunete și când dau pe DISC/USB/CARD se închide. Doar pe auxiliar și radio nu se închide. Cum se manifestă: https://streamable.com/smtgh Imagini:

Toata lumea e destul de increzatoare ca BTC isi va reveni... Eu cred ca 2018 va fi anul in care BTC va pierde primul loc pe coinmarketcap.

1 ora - putere mica de cumparare - trend pe short 2 ore - trend pe short direct full 4 ore - putere mica de cumparare - trend pe short 1 zi - 2 lumanri pe short - putere infima de rezist (buy) - trend pe short 1 saptamana - nici nu e de comentat 3 lumanari imense de short din 07.01 pana acum - TREND PE SHORT btc/usd inca e bullish, faceti scalp doar pe 30 minute daca aveti timp si chef, mai dureaza nitel pana isi revin lucrurile pe la orezari, e bun si buy acum daca va tine marginea...eu unul mai astept pana la sub 9,8k si fac buy, dar ma si reorientez pe drum daca e cazul, ca la cat de volatila e piata poate apar "greii" pe buy. spor

In UK nu iti recunoaste nimeni diplomele de la Laba SRL aia din Romania. Iti trebuie diplomele de la scoala. Eventual vezi ce poti lua de pe Brainbench. (ai sanse mult mai mari sa-ti fie recunoscute) Fara suparare, vezi ce spun si colegii de pe forum

Eu am firma si nu am consultat pe nimeni ca s-o deschid si nici nu am automatici si matematici si nu stiu ce uber skill-uri. Invat in permanenta cate ceva si incerc sa ma perfectionez. O firma te motiveaza sa tragi mai tare, sa dai din coate, sa vrei mai multe de la tine. Tot o ardeti cu diplomele vietii, mai da-le in pla de diplome. Puneti-vi-le in rame si atarnati-le pe pereti! Am bacu' si am facut cursuri acreditate de Ministerul Invatamantului ca sa-mi pot deschide firma, desi la PFA dpdv legal ar trebui sa poti desfasura orice activitate, exceptandu-le pe cele cu regim special (servicii medicale, detectivi, etc), NUMAI CA NU E ASA PT. CA ROMANIA! Am fost si la Poli, am incercat, o mizerie. Fumuri multe, profesori ceausisti cu gandire obtuza. Unii care vin cu hartii ingalbenite de timp si predau de pe ele. Cursurile platite pe acolo sunt la fel ca budele din cadrul facultatii. Eu ca sa-mi dau seama unde sunt intr-un loc, prima oara ma duc la buda (true fact). Cum plm sa inveti calculatoare pe hartie, cum? Ca sa nu mai zic ca in primii 2 ani bagi matematica la Greuceanu. Suntem in 2018 in pula calului, lasati fitilele astea cu te formeaza ca individ, te ajuta s-o decalotezi mai bine. De parca traim vreo 200-300 de ani. Totul se intampla cu repeziciune. Cu aprofundarea asta exacerbata ramai sarac si ajungi batran sau te faci profesor. Inclusiv in constructii in domeniul proiectarii sunt soft-uri extraordinare cum e Nemetschek Allplan care calculeaza tot (incarcari, rezistente,etc) nu mai stai sa faci calcule si proiecte pe hartie, cu rigla, compas. Mereti la facultati, stati pe banii parintilor, ca sa aveti primul job la 25-30 de ani. Pe urma toti angajatorii or sa invoce treaba asta cu lipsa de experienta si o sa lucrati cel putin 1 an pe un post de junior pe 2000-3000 de ron, dar "nici o problema ca veti putea castiga pana la 2000 de euro, se avanseaza foarte repede, totul depinde numai de voi." Nu haliti kkt-urile astea si nu faceti greseala sa intrati in hora asta a angajatorilor.

Cineva trebuie sa dezvolte acele CMS-uri, cine le foloseste trebuie sa le configureze, sa integreze mai multe tehnologii, sa rezolve probleme care apar ulterior. Nu e un lucru rau sa apara tehnologii care iti usureaza munca. Asta inseamna evolutia. E greu sa speculezi cand vine vorba de tehnologie, pentru ca evolueaza asa rapid, dar daca ne uitam in urma, se migreaza spre web. E mai usor sa accesezi un URL decat sa tot instalezi aplicatii si programe, si sa le si updatezi constant. Uita-te la site-urile gigant din ziua de azi cat sunt de complexe si cate tehnologii folosesc in spate. Nu se poate face asa ceva folosind doar niste CMS-uri out of the box. Invata tehnologiile folosite la ora actuala. Tehnologia se schimba si evolueaza, ceea ce e normal. Ceea ce faci acum in cateva zile, in viitor o sa poti face in cateva minute. Tot timpul apar alte layere peste cele deja existente, cu care o sa trebuiasca sa lucrezi. Cel mai important e sa stapanesti ceea ce exista acum, ca sa te poti adapta la ceea ce urmeaza. Totul este intr-o continua schimbare, si nimic nu ramane permanent.