Digital images have long been part of everyday life on social media, in applications, in journalism or on sales platforms. With the spread of AI-based tools for image processing, dealing with photos is changing fundamentally technically, legally and socially. What used to need experience with Photoshop succeeds today with just a few clicks in the browser. But where AI intervenes, there are also questions in the room: to authenticity, copyright and effect on perception.
Digital image processing: new tools, new possibilities
Who today Images online Edited, land quickly with AI-supported tools. Platforms such as Canva, Adobe Firefly or Deepai make it possible to remove backgrounds, exchange objects or change moods – often in a matter of seconds. Not only is retouching, but also re -generated. The special thing about it is that many applications work directly in the browser and without additional software installation, and use powerful AI models in the cloud.
The entry is low -threshold, the results are often surprising. But the image material generated raises questions – for example, when the facial photo is soft, the skin is smoother, the smile becomes wider. Or if entire window fronts are added to real estate pictures that have never existed in reality.
What is still real? Psychological effects of AI retouching
AI-based image processing not only affects the look of an image, but also its effect on viewers. Studies from media psychology show that slight changes in the face, such as a larger iris or smoother skin, can increase trust in the person depicted.
At the same time, the frequency of such processing increases the expectations: appearance, surroundings, lifestyle. In social networks, this leads to distorted self -pictures, especially among younger users. The border between “optimized” and “fake” blurs.
Between law and responsibility: author, licenses and transparency
Legally, the location is complex. If an existing picture is changed by AI, the question arises of copyright: who is an author – the AI, the person who promptly enters the original photographer?
It gets even more complicated with Deepfkes or completely AI-generated content. So far, such pictures have only been regulated to a limited extent. While journalistic media are bound to truth and transparency, there is a large scope for private applications. Nevertheless, in the event of abuse, legal aftermath threatens. An example of this would be a defamatory manipulation of the image.
Platform operators are also responsible: they have to make it clear when generated content is published. Some services automatically mark AI content, others leave it up to users.
Change tools: From the retouching to image synthesis
Classic image editing was a craft for a long time: selecting, masking, improving. With AI, the process changes fundamentally. It is no longer just about correcting an existing image, but increasingly about creating new content based on short text inputs (“prompting”).
This technique, also called “text-to-image”, leaves a simple description such as “An empty beach at sunset, with a person in the red dress” A completely new picture is created. This not only creates a new aesthetics, but also a new relationship with the truth of the image.
The technology behind it is usually based on neuronal networks such as stable diffusion or dall · e. They analyze large image text data sets and “learning” which are visual elements of certain descriptions. The results are often amazing and hardly recognizable as artificial.
Scientific benefits and ethical questions
Apart from popular applications in marketing or social media, AI image processing is also used in science. In medicine, for example, image data of CT scans are reworked with AI to recognize tumors earlier. In archeology, AI from fragments reconstructs historical buildings.
At the same time, the use raises questions: What distortions arise from the training of the algorithms on certain data records? Are certain groups systematically poorer displayed? And how can ethical guardrails be set when machines increasingly have a say in what to look like? Research institutes advocate particularly in sensitive areas of application for transparent data sources, comprehensible algorithms and human control.
The factor of trust: How visual AI perception changes
Images are often considered particularly credible – according to the motto “What you see is also true”. But the further AI processing spreads, the more this assumption is questioned.
When political images, application photos or supposed crime scene recordings are manipulated, trust in visual media is faltering overall. This does not only affect individual images, but the visual culture as a whole: authenticity becomes a difficult -to -verify criterion.
Some initiatives try to counteract, for example through digital watermarks, image forensics or transparent metadata. However, as long as such standards are not used across the board, the problem remains.
Between innovation and responsibility
In online image editing, AI offers great potential-from creative design to barrier-free use to scientific progress. At the same time, uncertainties about what is allowed arise about dealing with manipulated images and the effect on self -image and society.
How practice develops does not depend solely on technology and law, but also on how attentive and reflected with the new possibilities. What matters is not just what a picture shows. Is just as important what it does not hesitate.
