With a very rapid increase in deepfakes and digital image forgeries, ensuring the authenticity of images is becoming increasingly challenging. This report introduces a forgery detection framework that combines spatial and frequency-based features for detecting forgeries. We propose a dual branch convolution neural network that operates on features extracted from spatial and frequency domains. Features from both branches are fused and compared within a Siamese network, yielding 64 dimensional embed-dings for classification. When benchmarked on CASIA 2.0 dataset, our method achieves an accuracy of 77.9%, outperforming traditional statistical methods. Despite its relatively weaker performance compared to larger, more complex forgery detection pipelines, our approach balances computational complexity and detection reliability, making it ready for practical deployment. It provides a strong methodology for forensic scrutiny of digital images.

There's a Japanese word, komorebi, that describes beams of light and dappled shadows that result when the sun shines through trees. When I take my dog on walks around my leafy neighborhood in Washington, D.C., komorebi is what most often catches my eye, especially in this autumnal moment when dense, green summer foliage is starting to thin and turn golden. As the sun sets and the shadows grow long on the edge of a precipitous valley near my apartment, the foliage creates fluttering patterns of warm and cool colors. I try to photograph these apparitions with my iPhone camera, but I'm always disappointed in the results: the device's automated image processing treats contrast as a problem to be solved, aggressively darkening the highlights and lightening up the shadows to achieve a bland flatness. Little of the lambent atmosphere I see in real life survives in the image.

On August 27, 1960, at the Olympics in Rome, one of the most controversial gold medals was awarded. At the 100-meter freestyle men's swimming event, Australian swimmer John Devitt and American Lance Larson both recorded the same finish time of 55.2 seconds. Only Devitt walked away with the gold medal. The way swimming was timed was by using three timers per lane, all with stopwatches, from which an average was taken. In the rare occurrence there was a tie, a head judge, in this case Hans Runströmer from Sweden, was on hand to adjudicate.

Artificial intelligence is feared to one day take over humanity, but as for now people are using it to create stunning pieces of artwork that are now hanging in the first gallery inspired by Dalle-E - an AI-powered system that generates digital images through text inputs. The artwork, which is physically on display in San Francisco, was created by the'artist' inputting specific terms or selecting recommendations from the AI - all the pieces are for sale, with one for $5,000. However, one of the sculptures was created by reading the creators brainwaves and body signals to choose an initial AI-generated image that led to the finish piece. The gallery has been met with controversy as traditional artists do not accept the digital images as true art, noting it does not have the same hallmark of human creativity. Human engineers, however, note that there is more that goes into creating the AI-generated pieces, such as tweaking and refining specific options and features to create a perfect picture.

In the broadest sense, it is the ability of computers to interpret and understand digital images. This includes everything from identifying objects in an image to understanding the meaning of an image. Computer vision is a rapidly growing field with many potential applications. It is already being used in a number of industries, including healthcare, automotive, and security. And as the technology continues to develop, the potential uses for computer vision are only going to increase.

In brief Everyone agrees that text-to-image models are here to stay, though opinions are divided over AI-generated art.… Some artists are enthralled by the ability to create completely new digital images using text prompts and see it as a new tool to be creative. Other artists, however, detest the technology and believe it will take away their jobs and devalue their work. A machine can be trained to recreate a particular artist's style and outpace human artists, as RJ Palmer, a concept artist, told the BBC. "Right now, if an artist wants to copy my style, they might spend a week trying to replicate it. That's one person spending a week to create one thing. With this machine, you can produce hundreds of them a week/" AI is "directly stealing their essence in a way", and artists are currently powerless to stop it from happening.

In brief Everyone agrees that text-to-image models are here to stay, though opinions are divided over AI-generated art. Some artists are enthralled by the ability to create completely new digital images using text prompts and see it as a new tool to be creative. Other people who make their living from art, however, detest the technology – believing it will cost them their jobs and devalue their work. A machine can be trained to recreate a particular artist's style and outpace human artists, as RJ Palmer, a conceptual artist, told the BBC. "Right now, if an artist wants to copy my style, they might spend a week trying to replicate it. That's one person spending a week to create one thing. With this machine, you can produce hundreds of them a week."

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over traditional analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as aliasing, which can occur when an analog image is converted to digital form. Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic compression methods, such as the discrete cosine transform and adaptive Huffman coding.

Key Technology introduces AI-driven FM alert software for its digital sorting systems. This powerful tool captures and saves digital images of critical foreign material (FM) contaminants that the sorter detects and rejects from the product stream. Data outputs from the software can be utilized to immediately alert operators and/or signal a downstream device. AI-enhanced FM Alert helps processors better control FM and improve documentation to protect food safety. "Thanks to the application of advanced artificial intelligence, our new FM Alert software achieves uniquely accurate results – identifying, recording, and acting on true FM findings on the line," said Marco Azzaretti, director of marketing at Key. "The food processing industry continues to focus more and more on elevating food safety. By making product safer, this effective FM-fighting tool helps customers protect their brand's reputation and avoid costly recalls. Every food processor wants to prevent contamination, making FM Alert universally beneficial across all applications."

Image forensics is an investigation of digital images to identify manipulations that have been done on them. Nowadays, due to the availability of different low-cost devices for capturing images, digital images are gaining quite a bit of popularity. It occurs frequently that these images are manipulated by mistake or on purpose, resulting in inaccurate information being presented by the image. There is a need to develop techniques to identify forgeries present in digital images used by the media, in court trials, and for maintaining visual records, since digital images are commonly used as evidence through the media, in court, and for maintaining record keeping. A detailed review of various image forgery detection techniques is presented in this article including comparisons between the various methods, pros and cons, and results obtained during the experimentation.