A pair of hands holds a digital camera. "NUCA" is written in the hood above the lens and a black grip is on the right hand side of the device (left side of image). The camera body is off-white 3D printed plastic. The background is a pastel yellow.

AI Camera Only Takes Nudes

April 22, 2024 by Navarre Bartz 52 Comments

One of the cringier aspects of AI as we know it today has been the proliferation of deepfake technology to make nude photos of anyone you want. What if you took away the abstraction and put the faker and subject in the same space? That’s the question the NUCA camera was designed to explore. [via 404 Media]

[Mathias Vef] and [Benedikt Groß] designed the NUCA camera “with the intention of critiquing the current trajectory of AI image generation.” The camera itself is a fairly unassuming device, a 3D-printed digital camera (19.5 × 6 × 1.5 cm) with a 37 mm lens. When the camera shutter button is pressed, a nude image is generated of the subject.

The final image is generated using a mixture of the picture taken of the subject, pose data, and facial landmarks. The photo is run through a classifier which identifies features such as age, gender, body type, etc. and then uses those to generate a text prompt for Stable Diffusion. The original face of the subject is then stitched onto the nude image and aligned with the estimated pose. Many of the sample images on the project’s website show the bias toward certain beauty ideals from AI datasets.

Looking for more ways to use AI with cameras? How about this one that uses GPS to imagine a scene instead. Prefer to keep AI out of your endeavors to invade personal space? How about building your own TSA body scanner?

Can You Remembrandt Where This Is From?

August 5, 2020 by Sharon Lin 8 Comments

A group of researchers have built an algorithm for finding hidden connections in artwork.

The team, comprised of computer scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Microsoft, used paintings from the Metropolitan Museum of Art and Amsterdam’s Rijksmuseum to demonstrate these hidden connections, which link artwork that shares similar styles, such as Francisco de Zurbarán’s The Martyrdom of Saint Serapion (above left) and Jan Asselijn’s The Threatened Swan (above right). They were initially inspired by the “Rembrandt and Velazquez” exhibition in the Rijksmuseum, which demonstrated similarities between the artists’ work despite the former hailing from the Protestant Netherlands and the latter from Catholic Spain.

The algorithm, dubbed “MosAIc”, differs from probabilistic generative adversarial network (GAN)-based projects that generate artwork since it focuses on image retrieval instead. Rather than focusing solely on obvious factors such as color and style, the algorithm also tries to uncover meaning and theme. It does this by constructing a data structure called a conditional k-nearest neighbor (KNN) tree, which provides a tree-like structure where branches off a central image indicate similarity to the image. In order to query the data structure, these branches are followed until the closest match to an image in a dataset is found. In further iterations, it prunes unpromising branches in order to improve its time for new queries.

Some results from running the algorithm against museum collections were finding similarities between the Dutch Double Face Banyan and a Chinese ceramic figurine, traced to the flow of porcelain and iconography from the Chinese to the Dutch in the 16th to 20th centuries.

A surprising result of this study was discovering that the approach could also be applied to find problems with deep nerual networks, which are used for creating deepfakes. While GANs can often have blind spots in their models, struggling to recreate certain classes of photos, MosAIc was able to overcome these shortcomings and accurately reproduce realistic images.

While the team admits that their implementation isn’t the most optimized version of KNN, their main objective was to present a broad conditioning scheme that is simple but effective for applications. Their hope is to inspire related researchers to consider multi-disciplinary applications for algorithms.

This Week In Security: Simjacker, Microsoft Updates, Apple Vs Google, Audio DeepFakes, And NetCAT

September 13, 2019 by Jonathan Bennett 20 Comments

We often think of SIM cards as simple data storage devices, but in reality a SIM card is a miniature Universal integrated circuit card, or smart card. Subscriber data isn’t a simple text string, but a program running on the smart cards tiny processor, acting as a hardware cryptographic token. The presence of this tiny processor in everyone’s cell phone was eventually put to use in the form of the Sim application ToolKit (STK), which allowed cell phone networks to add services to very basic cell phones, such as mobile banking and account management.

Legacy software running in a place most of us have forgotten about? Sounds like it’s ripe for exploitation. The researchers at Adaptive Mobile Security discovered that exploitation of SMS messages has been happening for quite some time. In an era of complicated and sophisticated attacks, Simjacker seems almost refreshingly simple. An execution environment included on many sim cards, the S@T Browser, can request data from the cell phone’s OS, and even send SMS messages. The attacker simply sends an SMS to this environment containing instructions to request the phones unique identifier and current GPS location, and send that information back in another SMS message.

It’s questionable whether there is actually an exploit here, as it seems the S@T Browser is just insecure by design. Either way, the fact that essentially anyone can track a cell phone simply by sending a special SMS message to that phone is quite a severe problem. Continue reading “This Week In Security: Simjacker, Microsoft Updates, Apple Vs Google, Audio DeepFakes, And NetCAT” →